id	sid	tid	token	lemma	pos
geusb-8353	1	1	method	method	NOUN
geusb-8353	1	2	article	article	NOUN
geusb-8353	1	3	|	|	ADP
geusb-8353	1	4	short	short	ADJ
geusb-8353	1	5	falk	falk	NOUN
geusb-8353	1	6	&	&	CCONJ
geusb-8353	1	7	madsen	madsen	PROPN
geusb-8353	1	8	2023	2023	NUM
geusb-8353	1	9	:	:	PUNCT
geusb-8353	1	10	geus	geus	NOUN
geusb-8353	1	11	bulletin	bulletin	NOUN
geusb-8353	1	12	53	53	NUM
geusb-8353	1	13	.	.	PUNCT
geusb-8353	1	14	8353	8353	NUM
geusb-8353	1	15	.	.	PUNCT
geusb-8353	2	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	2	2	1	1	NUM
geusb-8353	2	3	of	of	ADP
geusb-8353	2	4	7	7	NUM
geusb-8353	2	5	machine	machine	NOUN
geusb-8353	2	6	learning	learning	NOUN
geusb-8353	2	7	-	-	PUNCT
geusb-8353	2	8	based	base	VERB
geusb-8353	2	9	estimation	estimation	NOUN
geusb-8353	2	10	and	and	CCONJ
geusb-8353	2	11	clustering	clustering	NOUN
geusb-8353	2	12	of	of	ADP
geusb-8353	2	13	statistics	statistic	NOUN
geusb-8353	2	14	within	within	ADP
geusb-8353	2	15	stratigraphic	stratigraphic	ADJ
geusb-8353	2	16	models	model	NOUN
geusb-8353	2	17	as	as	SCONJ
geusb-8353	2	18	exemplified	exemplify	VERB
geusb-8353	2	19	in	in	ADP
geusb-8353	2	20	denmark	denmark	PROPN
geusb-8353	2	21	frederik	frederik	PROPN
geusb-8353	2	22	alexander	alexander	PROPN
geusb-8353	2	23	falk*1	falk*1	PROPN
geusb-8353	2	24	,	,	PUNCT
geusb-8353	2	25	rasmus	rasmus	PROPN
geusb-8353	2	26	bødker	bødker	PROPN
geusb-8353	2	27	madsen2	madsen2	VERB
geusb-8353	2	28	1department	1department	NUM
geusb-8353	2	29	of	of	ADP
geusb-8353	2	30	geoscience	geoscience	NOUN
geusb-8353	2	31	,	,	PUNCT
geusb-8353	2	32	aarhus	aarhus	PROPN
geusb-8353	2	33	university	university	PROPN
geusb-8353	2	34	,	,	PUNCT
geusb-8353	2	35	aarhus	aarhus	PROPN
geusb-8353	2	36	,	,	PUNCT
geusb-8353	2	37	denmark	denmark	PROPN
geusb-8353	2	38	;	;	PUNCT
geusb-8353	2	39	2department	2department	NUM
geusb-8353	2	40	of	of	ADP
geusb-8353	2	41	near	near	PROPN
geusb-8353	2	42	surface	surface	NOUN
geusb-8353	2	43	land	land	NOUN
geusb-8353	2	44	and	and	CCONJ
geusb-8353	2	45	marine	marine	ADJ
geusb-8353	2	46	geology	geology	NOUN
geusb-8353	2	47	,	,	PUNCT
geusb-8353	2	48	geological	geological	ADJ
geusb-8353	2	49	survey	survey	NOUN
geusb-8353	2	50	of	of	ADP
geusb-8353	2	51	denmark	denmark	NOUN
geusb-8353	2	52	and	and	CCONJ
geusb-8353	2	53	greenland	greenland	PROPN
geusb-8353	2	54	(	(	PUNCT
geusb-8353	2	55	geus	geus	NOUN
geusb-8353	2	56	)	)	PUNCT
geusb-8353	2	57	,	,	PUNCT
geusb-8353	2	58	aarhus	aarhus	PROPN
geusb-8353	2	59	,	,	PUNCT
geusb-8353	2	60	denmark	denmark	NOUN
geusb-8353	2	61	abstract	abstract	ADJ
geusb-8353	2	62	estimating	estimate	VERB
geusb-8353	2	63	a	a	DET
geusb-8353	2	64	covariance	covariance	NOUN
geusb-8353	2	65	model	model	NOUN
geusb-8353	2	66	for	for	ADP
geusb-8353	2	67	kriging	krige	VERB
geusb-8353	2	68	purposes	purpose	NOUN
geusb-8353	2	69	is	be	AUX
geusb-8353	2	70	traditionally	traditionally	ADV
geusb-8353	2	71	done	do	VERB
geusb-8353	2	72	using	use	VERB
geusb-8353	2	73	semivariogram	semivariogram	NOUN
geusb-8353	2	74	analyses	analysis	NOUN
geusb-8353	2	75	,	,	PUNCT
geusb-8353	2	76	where	where	SCONJ
geusb-8353	2	77	an	an	DET
geusb-8353	2	78	empirical	empirical	ADJ
geusb-8353	2	79	semivariogram	semivariogram	NOUN
geusb-8353	2	80	is	be	AUX
geusb-8353	2	81	calculated	calculate	VERB
geusb-8353	2	82	,	,	PUNCT
geusb-8353	2	83	and	and	CCONJ
geusb-8353	2	84	a	a	DET
geusb-8353	2	85	chosen	choose	VERB
geusb-8353	2	86	semivariogram	semivariogram	NOUN
geusb-8353	2	87	model	model	NOUN
geusb-8353	2	88	,	,	PUNCT
geusb-8353	2	89	usually	usually	ADV
geusb-8353	2	90	defined	define	VERB
geusb-8353	2	91	by	by	ADP
geusb-8353	2	92	a	a	DET
geusb-8353	2	93	sill	sill	NOUN
geusb-8353	2	94	and	and	CCONJ
geusb-8353	2	95	a	a	DET
geusb-8353	2	96	range	range	NOUN
geusb-8353	2	97	,	,	PUNCT
geusb-8353	2	98	is	be	AUX
geusb-8353	2	99	fitted	fit	VERB
geusb-8353	2	100	.	.	PUNCT
geusb-8353	3	1	we	we	PRON
geusb-8353	3	2	demonstrate	demonstrate	VERB
geusb-8353	3	3	that	that	SCONJ
geusb-8353	3	4	a	a	DET
geusb-8353	3	5	convolutional	convolutional	ADJ
geusb-8353	3	6	neural	neural	ADJ
geusb-8353	3	7	network	network	NOUN
geusb-8353	3	8	can	can	AUX
geusb-8353	3	9	estimate	estimate	VERB
geusb-8353	3	10	such	such	DET
geusb-8353	3	11	a	a	DET
geusb-8353	3	12	semivariogram	semivariogram	NOUN
geusb-8353	3	13	model	model	NOUN
geusb-8353	3	14	with	with	ADP
geusb-8353	3	15	comparable	comparable	ADJ
geusb-8353	3	16	accuracy	accuracy	NOUN
geusb-8353	3	17	and	and	CCONJ
geusb-8353	3	18	precision	precision	NOUN
geusb-8353	3	19	by	by	ADP
geusb-8353	3	20	training	train	VERB
geusb-8353	3	21	it	it	PRON
geusb-8353	3	22	to	to	PART
geusb-8353	3	23	recognise	recognise	VERB
geusb-8353	3	24	the	the	DET
geusb-8353	3	25	relationship	relationship	NOUN
geusb-8353	3	26	between	between	ADP
geusb-8353	3	27	realisations	realisation	NOUN
geusb-8353	3	28	of	of	ADP
geusb-8353	3	29	gaussian	gaussian	ADJ
geusb-8353	3	30	random	random	ADJ
geusb-8353	3	31	fields	field	NOUN
geusb-8353	3	32	and	and	CCONJ
geusb-8353	3	33	the	the	DET
geusb-8353	3	34	sill	sill	ADJ
geusb-8353	3	35	and	and	CCONJ
geusb-8353	3	36	range	range	NOUN
geusb-8353	3	37	values	value	NOUN
geusb-8353	3	38	that	that	PRON
geusb-8353	3	39	define	define	VERB
geusb-8353	3	40	it	it	PRON
geusb-8353	3	41	,	,	PUNCT
geusb-8353	3	42	for	for	ADP
geusb-8353	3	43	a	a	DET
geusb-8353	3	44	gaussian	gaussian	ADJ
geusb-8353	3	45	type	type	NOUN
geusb-8353	3	46	semivariance	semivariance	NOUN
geusb-8353	3	47	model	model	NOUN
geusb-8353	3	48	.	.	PUNCT
geusb-8353	4	1	we	we	PRON
geusb-8353	4	2	do	do	VERB
geusb-8353	4	3	this	this	PRON
geusb-8353	4	4	by	by	ADP
geusb-8353	4	5	training	train	VERB
geusb-8353	4	6	the	the	DET
geusb-8353	4	7	network	network	NOUN
geusb-8353	4	8	with	with	ADP
geusb-8353	4	9	synthetic	synthetic	ADJ
geusb-8353	4	10	data	datum	NOUN
geusb-8353	4	11	consisting	consist	VERB
geusb-8353	4	12	of	of	ADP
geusb-8353	4	13	many	many	ADJ
geusb-8353	4	14	such	such	ADJ
geusb-8353	4	15	realisations	realisation	NOUN
geusb-8353	4	16	with	with	ADP
geusb-8353	4	17	the	the	DET
geusb-8353	4	18	sill	sill	ADJ
geusb-8353	4	19	and	and	CCONJ
geusb-8353	4	20	range	range	VERB
geusb-8353	4	21	as	as	ADP
geusb-8353	4	22	the	the	DET
geusb-8353	4	23	target	target	NOUN
geusb-8353	4	24	variables	variable	NOUN
geusb-8353	4	25	.	.	PUNCT
geusb-8353	5	1	because	because	SCONJ
geusb-8353	5	2	training	training	NOUN
geusb-8353	5	3	takes	take	VERB
geusb-8353	5	4	time	time	NOUN
geusb-8353	5	5	,	,	PUNCT
geusb-8353	5	6	the	the	DET
geusb-8353	5	7	method	method	NOUN
geusb-8353	5	8	is	be	AUX
geusb-8353	5	9	best	well	ADV
geusb-8353	5	10	suited	suit	VERB
geusb-8353	5	11	for	for	ADP
geusb-8353	5	12	cases	case	NOUN
geusb-8353	5	13	where	where	SCONJ
geusb-8353	5	14	many	many	ADJ
geusb-8353	5	15	models	model	NOUN
geusb-8353	5	16	need	need	VERB
geusb-8353	5	17	to	to	PART
geusb-8353	5	18	be	be	AUX
geusb-8353	5	19	estimated	estimate	VERB
geusb-8353	5	20	since	since	SCONJ
geusb-8353	5	21	the	the	DET
geusb-8353	5	22	actual	actual	ADJ
geusb-8353	5	23	estimation	estimation	NOUN
geusb-8353	5	24	itself	itself	PRON
geusb-8353	5	25	is	be	AUX
geusb-8353	5	26	about	about	ADV
geusb-8353	5	27	70	70	NUM
geusb-8353	5	28	times	time	NOUN
geusb-8353	5	29	faster	fast	ADV
geusb-8353	5	30	with	with	ADP
geusb-8353	5	31	the	the	DET
geusb-8353	5	32	neural	neural	ADJ
geusb-8353	5	33	network	network	NOUN
geusb-8353	5	34	than	than	ADP
geusb-8353	5	35	with	with	ADP
geusb-8353	5	36	the	the	DET
geusb-8353	5	37	traditional	traditional	ADJ
geusb-8353	5	38	approach	approach	NOUN
geusb-8353	5	39	.	.	PUNCT
geusb-8353	6	1	we	we	PRON
geusb-8353	6	2	demonstrate	demonstrate	VERB
geusb-8353	6	3	the	the	DET
geusb-8353	6	4	viability	viability	NOUN
geusb-8353	6	5	of	of	ADP
geusb-8353	6	6	the	the	DET
geusb-8353	6	7	method	method	NOUN
geusb-8353	6	8	in	in	ADP
geusb-8353	6	9	three	three	NUM
geusb-8353	6	10	ways	way	NOUN
geusb-8353	6	11	:	:	PUNCT
geusb-8353	6	12	(	(	PUNCT
geusb-8353	6	13	1	1	X
geusb-8353	6	14	)	)	PUNCT
geusb-8353	6	15	we	we	PRON
geusb-8353	6	16	test	test	VERB
geusb-8353	6	17	the	the	DET
geusb-8353	6	18	model	model	NOUN
geusb-8353	6	19	’s	’s	PART
geusb-8353	6	20	performance	performance	NOUN
geusb-8353	6	21	on	on	ADP
geusb-8353	6	22	the	the	DET
geusb-8353	6	23	validation	validation	NOUN
geusb-8353	6	24	data	datum	NOUN
geusb-8353	6	25	,	,	PUNCT
geusb-8353	6	26	(	(	PUNCT
geusb-8353	6	27	2	2	X
geusb-8353	6	28	)	)	PUNCT
geusb-8353	6	29	we	we	PRON
geusb-8353	6	30	do	do	VERB
geusb-8353	6	31	a	a	DET
geusb-8353	6	32	test	test	NOUN
geusb-8353	6	33	where	where	SCONJ
geusb-8353	6	34	we	we	PRON
geusb-8353	6	35	compare	compare	VERB
geusb-8353	6	36	the	the	DET
geusb-8353	6	37	model	model	NOUN
geusb-8353	6	38	to	to	ADP
geusb-8353	6	39	the	the	DET
geusb-8353	6	40	traditional	traditional	ADJ
geusb-8353	6	41	approach	approach	NOUN
geusb-8353	6	42	and	and	CCONJ
geusb-8353	6	43	(	(	PUNCT
geusb-8353	6	44	3	3	X
geusb-8353	6	45	)	)	PUNCT
geusb-8353	6	46	we	we	PRON
geusb-8353	6	47	show	show	VERB
geusb-8353	6	48	an	an	DET
geusb-8353	6	49	example	example	NOUN
geusb-8353	6	50	of	of	ADP
geusb-8353	6	51	an	an	DET
geusb-8353	6	52	actual	actual	ADJ
geusb-8353	6	53	application	application	NOUN
geusb-8353	6	54	of	of	ADP
geusb-8353	6	55	the	the	DET
geusb-8353	6	56	method	method	NOUN
geusb-8353	6	57	using	use	VERB
geusb-8353	6	58	the	the	DET
geusb-8353	6	59	danish	danish	ADJ
geusb-8353	6	60	national	national	ADJ
geusb-8353	6	61	hydrostratigraphic	hydrostratigraphic	ADJ
geusb-8353	6	62	model	model	NOUN
geusb-8353	6	63	.	.	PUNCT
geusb-8353	7	1	*	*	PUNCT
geusb-8353	7	2	correspondence	correspondence	NOUN
geusb-8353	7	3	:	:	PUNCT
geusb-8353	7	4	frederikfalk@geo.au.dk	frederikfalk@geo.au.dk	NOUN
geusb-8353	7	5	received	receive	VERB
geusb-8353	7	6	:	:	PUNCT
geusb-8353	7	7	31	31	NUM
geusb-8353	7	8	may	may	AUX
geusb-8353	7	9	2023	2023	NUM
geusb-8353	7	10	revised	revise	VERB
geusb-8353	7	11	:	:	PUNCT
geusb-8353	7	12	23	23	NUM
geusb-8353	7	13	aug	aug	PROPN
geusb-8353	7	14	2023	2023	NUM
geusb-8353	7	15	accepted	accept	VERB
geusb-8353	7	16	:	:	PUNCT
geusb-8353	7	17	26	26	NUM
geusb-8353	7	18	sep	sep	NOUN
geusb-8353	7	19	2023	2023	NUM
geusb-8353	7	20	published	publish	VERB
geusb-8353	7	21	:	:	PUNCT
geusb-8353	7	22	17	17	NUM
geusb-8353	7	23	nov	nov	NOUN
geusb-8353	7	24	2023	2023	NUM
geusb-8353	7	25	keywords	keyword	NOUN
geusb-8353	7	26	:	:	PUNCT
geusb-8353	7	27	convolutional	convolutional	ADJ
geusb-8353	7	28	neural	neural	ADJ
geusb-8353	7	29	network	network	NOUN
geusb-8353	7	30	,	,	PUNCT
geusb-8353	7	31	covariance	covariance	NOUN
geusb-8353	7	32	model	model	NOUN
geusb-8353	7	33	,	,	PUNCT
geusb-8353	7	34	semivariogram	semivariogram	NOUN
geusb-8353	7	35	modelling	modelling	NOUN
geusb-8353	7	36	,	,	PUNCT
geusb-8353	7	37	machine	machine	NOUN
geusb-8353	7	38	learning	learning	NOUN
geusb-8353	7	39	,	,	PUNCT
geusb-8353	7	40	local	local	ADJ
geusb-8353	7	41	stationarity	stationarity	NOUN
geusb-8353	7	42	abbreviations	abbreviation	NOUN
geusb-8353	7	43	cnn	cnn	PROPN
geusb-8353	7	44	:	:	PUNCT
geusb-8353	7	45	convolutional	convolutional	ADJ
geusb-8353	7	46	neural	neural	ADJ
geusb-8353	7	47	network	network	NOUN
geusb-8353	7	48	ml	ml	VERB
geusb-8353	7	49	:	:	PUNCT
geusb-8353	7	50	machine	machine	NOUN
geusb-8353	7	51	learning	learn	VERB
geusb-8353	7	52	nn	nn	PROPN
geusb-8353	7	53	:	:	PUNCT
geusb-8353	7	54	neural	neural	ADJ
geusb-8353	7	55	network	network	NOUN
geusb-8353	7	56	geus	geus	NOUN
geusb-8353	7	57	bulletin	bulletin	NOUN
geusb-8353	7	58	(	(	PUNCT
geusb-8353	7	59	eissn	eissn	NOUN
geusb-8353	7	60	:	:	PUNCT
geusb-8353	7	61	2597	2597	NUM
geusb-8353	7	62	-	-	SYM
geusb-8353	7	63	2154	2154	NUM
geusb-8353	7	64	)	)	PUNCT
geusb-8353	7	65	is	be	AUX
geusb-8353	7	66	an	an	DET
geusb-8353	7	67	open	open	ADJ
geusb-8353	7	68	access	access	NOUN
geusb-8353	7	69	,	,	PUNCT
geusb-8353	7	70	peer	peer	NOUN
geusb-8353	7	71	-	-	PUNCT
geusb-8353	7	72	reviewed	review	VERB
geusb-8353	7	73	journal	journal	NOUN
geusb-8353	7	74	published	publish	VERB
geusb-8353	7	75	by	by	ADP
geusb-8353	7	76	the	the	DET
geusb-8353	7	77	geological	geological	ADJ
geusb-8353	7	78	survey	survey	NOUN
geusb-8353	7	79	of	of	ADP
geusb-8353	7	80	denmark	denmark	NOUN
geusb-8353	7	81	and	and	CCONJ
geusb-8353	7	82	greenland	greenland	PROPN
geusb-8353	7	83	(	(	PUNCT
geusb-8353	7	84	geus	geus	NOUN
geusb-8353	7	85	)	)	PUNCT
geusb-8353	7	86	.	.	PUNCT
geusb-8353	8	1	this	this	DET
geusb-8353	8	2	article	article	NOUN
geusb-8353	8	3	is	be	AUX
geusb-8353	8	4	distributed	distribute	VERB
geusb-8353	8	5	under	under	ADP
geusb-8353	8	6	a	a	DET
geusb-8353	8	7	cc	cc	NOUN
geusb-8353	8	8	-	-	PUNCT
geusb-8353	8	9	by	by	ADP
geusb-8353	8	10	4.0	4.0	NUM
geusb-8353	8	11	licence	licence	NOUN
geusb-8353	8	12	,	,	PUNCT
geusb-8353	8	13	permitting	permit	VERB
geusb-8353	8	14	free	free	ADJ
geusb-8353	8	15	redistribution	redistribution	NOUN
geusb-8353	8	16	,	,	PUNCT
geusb-8353	8	17	and	and	CCONJ
geusb-8353	8	18	reproduction	reproduction	NOUN
geusb-8353	8	19	for	for	ADP
geusb-8353	8	20	any	any	DET
geusb-8353	8	21	purpose	purpose	NOUN
geusb-8353	8	22	,	,	PUNCT
geusb-8353	8	23	even	even	ADV
geusb-8353	8	24	commercial	commercial	ADJ
geusb-8353	8	25	,	,	PUNCT
geusb-8353	8	26	provided	provide	VERB
geusb-8353	8	27	proper	proper	ADJ
geusb-8353	8	28	citation	citation	NOUN
geusb-8353	8	29	of	of	ADP
geusb-8353	8	30	the	the	DET
geusb-8353	8	31	original	original	ADJ
geusb-8353	8	32	work	work	NOUN
geusb-8353	8	33	.	.	PUNCT
geusb-8353	9	1	author(s	author(s	NOUN
geusb-8353	9	2	)	)	PUNCT
geusb-8353	9	3	retain	retain	VERB
geusb-8353	9	4	copyright	copyright	NOUN
geusb-8353	9	5	.	.	PUNCT
geusb-8353	10	1	edited	edit	VERB
geusb-8353	10	2	by	by	ADP
geusb-8353	10	3	:	:	PUNCT
geusb-8353	10	4	julian	julian	PROPN
geusb-8353	10	5	koch	koch	PROPN
geusb-8353	10	6	(	(	PUNCT
geusb-8353	10	7	geus	geus	NOUN
geusb-8353	10	8	,	,	PUNCT
geusb-8353	10	9	copenhagen	copenhagen	PROPN
geusb-8353	10	10	,	,	PUNCT
geusb-8353	10	11	denmark	denmark	PROPN
geusb-8353	10	12	)	)	PUNCT
geusb-8353	10	13	reviewed	review	VERB
geusb-8353	10	14	by	by	ADP
geusb-8353	10	15	:	:	PUNCT
geusb-8353	10	16	jacob	jacob	PROPN
geusb-8353	10	17	skauvold	skauvold	PROPN
geusb-8353	10	18	(	(	PUNCT
geusb-8353	10	19	norwegian	norwegian	ADJ
geusb-8353	10	20	computing	computing	NOUN
geusb-8353	10	21	center	center	NOUN
geusb-8353	10	22	,	,	PUNCT
geusb-8353	10	23	norway	norway	PROPN
geusb-8353	10	24	)	)	PUNCT
geusb-8353	10	25	funding	funding	NOUN
geusb-8353	10	26	:	:	PUNCT
geusb-8353	10	27	see	see	VERB
geusb-8353	10	28	page	page	NOUN
geusb-8353	10	29	7	7	NUM
geusb-8353	10	30	competing	compete	VERB
geusb-8353	10	31	interests	interest	NOUN
geusb-8353	10	32	:	:	PUNCT
geusb-8353	10	33	see	see	VERB
geusb-8353	10	34	page	page	NOUN
geusb-8353	10	35	7	7	NUM
geusb-8353	10	36	additional	additional	ADJ
geusb-8353	10	37	files	file	NOUN
geusb-8353	10	38	:	:	PUNCT
geusb-8353	10	39	none	none	NOUN
geusb-8353	10	40	tabular	tabular	VERB
geusb-8353	10	41	abstract	abstract	ADJ
geusb-8353	10	42	geographical	geographical	ADJ
geusb-8353	10	43	coverage	coverage	NOUN
geusb-8353	10	44	fyn	fyn	PROPN
geusb-8353	10	45	,	,	PUNCT
geusb-8353	10	46	denmark	denmark	PROPN
geusb-8353	10	47	temporal	temporal	ADJ
geusb-8353	10	48	coverage	coverage	NOUN
geusb-8353	10	49	n	n	CCONJ
geusb-8353	10	50	/	/	SYM
geusb-8353	10	51	a	a	PRON
geusb-8353	10	52	subject(s	subject(s	PROPN
geusb-8353	10	53	)	)	PUNCT
geusb-8353	10	54	covered	cover	VERB
geusb-8353	10	55	geophysics	geophysic	NOUN
geusb-8353	10	56	,	,	PUNCT
geusb-8353	10	57	computational	computational	ADJ
geusb-8353	10	58	geoscience	geoscience	NOUN
geusb-8353	10	59	,	,	PUNCT
geusb-8353	10	60	informatics	informatic	NOUN
geusb-8353	10	61	and	and	CCONJ
geusb-8353	10	62	remote	remote	ADJ
geusb-8353	10	63	sensing	sensing	NOUN
geusb-8353	10	64	method	method	NOUN
geusb-8353	10	65	type	type	NOUN
geusb-8353	10	66	a	a	DET
geusb-8353	10	67	new	new	ADJ
geusb-8353	10	68	machine	machine	NOUN
geusb-8353	10	69	learning	learning	NOUN
geusb-8353	10	70	-	-	PUNCT
geusb-8353	10	71	based	base	VERB
geusb-8353	10	72	method	method	NOUN
geusb-8353	10	73	for	for	ADP
geusb-8353	10	74	estimating	estimate	VERB
geusb-8353	10	75	locally	locally	ADV
geusb-8353	10	76	optimised	optimise	VERB
geusb-8353	10	77	semivariogram	semivariogram	NOUN
geusb-8353	10	78	parameters	parameter	NOUN
geusb-8353	10	79	for	for	ADP
geusb-8353	10	80	grid	grid	NOUN
geusb-8353	10	81	cells	cell	NOUN
geusb-8353	10	82	in	in	ADP
geusb-8353	10	83	stratigraphic	stratigraphic	ADJ
geusb-8353	10	84	models	model	NOUN
geusb-8353	10	85	followed	follow	VERB
geusb-8353	10	86	by	by	ADP
geusb-8353	10	87	clustering	cluster	VERB
geusb-8353	10	88	for	for	ADP
geusb-8353	10	89	the	the	DET
geusb-8353	10	90	introduction	introduction	NOUN
geusb-8353	10	91	of	of	ADP
geusb-8353	10	92	the	the	DET
geusb-8353	10	93	assumption	assumption	NOUN
geusb-8353	10	94	of	of	ADP
geusb-8353	10	95	local	local	ADJ
geusb-8353	10	96	stationarity	stationarity	NOUN
geusb-8353	10	97	.	.	PROPN
geusb-8353	11	1	method	method	PROPN
geusb-8353	11	2	name	name	NOUN
geusb-8353	11	3	machine	machine	NOUN
geusb-8353	11	4	learning	learning	NOUN
geusb-8353	11	5	-	-	PUNCT
geusb-8353	11	6	based	base	VERB
geusb-8353	11	7	semivariogram	semivariogram	NOUN
geusb-8353	11	8	model	model	NOUN
geusb-8353	11	9	estimation	estimation	NOUN
geusb-8353	11	10	and	and	CCONJ
geusb-8353	11	11	clustering	clustering	NOUN
geusb-8353	11	12	instruments	instrument	NOUN
geusb-8353	11	13	and	and	CCONJ
geusb-8353	11	14	equipment	equipment	NOUN
geusb-8353	11	15	used	use	VERB
geusb-8353	11	16	equipment	equipment	NOUN
geusb-8353	11	17	used	use	VERB
geusb-8353	11	18	:	:	PUNCT
geusb-8353	11	19	 	 	SPACE
geusb-8353	11	20	-	-	PUNCT
geusb-8353	11	21	 	 	SPACE
geusb-8353	11	22	a	a	DET
geusb-8353	11	23	sufficiently	sufficiently	ADV
geusb-8353	11	24	effective	effective	ADJ
geusb-8353	11	25	computer	computer	NOUN
geusb-8353	11	26	 	 	SPACE
geusb-8353	11	27	-	-	PUNCT
geusb-8353	11	28	 	 	SPACE
geusb-8353	11	29	matlab	matlab	PROPN
geusb-8353	11	30	®	®	NOUN
geusb-8353	11	31	software	software	NOUN
geusb-8353	11	32	license	license	NOUN
geusb-8353	11	33	   	   	SPACE
geusb-8353	11	34	•	•	NUM
geusb-8353	11	35	 	 	SPACE
geusb-8353	11	36	machine	machine	NOUN
geusb-8353	11	37	learning	learn	VERB
geusb-8353	11	38	toolbox	toolbox	NOUN
geusb-8353	11	39	for	for	ADP
geusb-8353	11	40	matlab	matlab	PROPN
geusb-8353	11	41	   	   	SPACE
geusb-8353	11	42	•	•	PROPN
geusb-8353	11	43	 	 	SPACE
geusb-8353	11	44	sippi	sippi	NOUN
geusb-8353	11	45	geostatistics	geostatistics	PROPN
geusb-8353	11	46	toolbox	toolbox	NOUN
geusb-8353	11	47	for	for	ADP
geusb-8353	11	48	matlab	matlab	PROPN
geusb-8353	11	49	   	   	SPACE
geusb-8353	11	50	•	•	PROPN
geusb-8353	11	51	 	 	SPACE
geusb-8353	11	52	mgstat	mgstat	NOUN
geusb-8353	11	53	geostatistics	geostatistics	PROPN
geusb-8353	11	54	toolbox	toolbox	NOUN
geusb-8353	11	55	for	for	ADP
geusb-8353	11	56	matlab	matlab	PROPN
geusb-8353	11	57	related	related	ADJ
geusb-8353	11	58	publications	publication	NOUN
geusb-8353	11	59	none	none	NOUN
geusb-8353	11	60	potential	potential	ADJ
geusb-8353	11	61	application(s	application(s	NOUN
geusb-8353	11	62	)	)	PUNCT
geusb-8353	11	63	for	for	ADP
geusb-8353	11	64	this	this	DET
geusb-8353	11	65	method	method	NOUN
geusb-8353	11	66	this	this	DET
geusb-8353	11	67	method	method	NOUN
geusb-8353	11	68	may	may	AUX
geusb-8353	11	69	be	be	AUX
geusb-8353	11	70	used	use	VERB
geusb-8353	11	71	to	to	PART
geusb-8353	11	72	infer	infer	VERB
geusb-8353	11	73	a	a	DET
geusb-8353	11	74	statistical	statistical	ADJ
geusb-8353	11	75	model	model	NOUN
geusb-8353	11	76	from	from	ADP
geusb-8353	11	77	one	one	NUM
geusb-8353	11	78	stratigraphic	stratigraphic	ADJ
geusb-8353	11	79	model	model	NOUN
geusb-8353	11	80	,	,	PUNCT
geusb-8353	11	81	which	which	PRON
geusb-8353	11	82	is	be	AUX
geusb-8353	11	83	useful	useful	ADJ
geusb-8353	11	84	for	for	ADP
geusb-8353	11	85	uncertainty	uncertainty	NOUN
geusb-8353	11	86	quantification	quantification	NOUN
geusb-8353	11	87	.	.	PUNCT
geusb-8353	12	1	the	the	DET
geusb-8353	12	2	method	method	NOUN
geusb-8353	12	3	is	be	AUX
geusb-8353	12	4	also	also	ADV
geusb-8353	12	5	useful	useful	ADJ
geusb-8353	12	6	for	for	ADP
geusb-8353	12	7	very	very	ADV
geusb-8353	12	8	fast	fast	ADJ
geusb-8353	12	9	semivariogram	semivariogram	NOUN
geusb-8353	12	10	modelling	model	VERB
geusb-8353	12	11	whenever	whenever	SCONJ
geusb-8353	12	12	the	the	DET
geusb-8353	12	13	advantage	advantage	NOUN
geusb-8353	12	14	of	of	ADP
geusb-8353	12	15	doing	do	VERB
geusb-8353	12	16	so	so	ADV
geusb-8353	12	17	outweighs	outweigh	VERB
geusb-8353	12	18	the	the	DET
geusb-8353	12	19	time	time	NOUN
geusb-8353	12	20	it	it	PRON
geusb-8353	12	21	takes	take	VERB
geusb-8353	12	22	to	to	PART
geusb-8353	12	23	train	train	VERB
geusb-8353	12	24	the	the	DET
geusb-8353	12	25	model	model	NOUN
geusb-8353	12	26	.	.	PUNCT
geusb-8353	13	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	13	2	https://orcid.org/0009-0003-1023-5161	https://orcid.org/0009-0003-1023-5161	VERB
geusb-8353	13	3	https://orcid.org/0000-0001-8538-7491	https://orcid.org/0000-0001-8538-7491	PROPN
geusb-8353	13	4	mailto:frederikfalk@geo.au.dk	mailto:frederikfalk@geo.au.dk	PROPN
geusb-8353	13	5	https://creativecommons.org/licenses/by/4.0/deed.ast	https://creativecommons.org/licenses/by/4.0/deed.ast	PROPN
geusb-8353	13	6	falk	falk	PROPN
geusb-8353	13	7	&	&	CCONJ
geusb-8353	13	8	madsen	madsen	PROPN
geusb-8353	13	9	2023	2023	NUM
geusb-8353	13	10	:	:	PUNCT
geusb-8353	13	11	geus	geus	NOUN
geusb-8353	13	12	bulletin	bulletin	NOUN
geusb-8353	13	13	53	53	NUM
geusb-8353	13	14	.	.	PUNCT
geusb-8353	13	15	8353	8353	NUM
geusb-8353	13	16	.	.	PUNCT
geusb-8353	14	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	14	2	2	2	NUM
geusb-8353	14	3	of	of	ADP
geusb-8353	14	4	7	7	NUM
geusb-8353	14	5	www.geusbul	www.geusbul	NOUN
geusb-8353	14	6	let	let	VERB
geusb-8353	14	7	in.org	in.org	ADJ
geusb-8353	14	8	introduction	introduction	NOUN
geusb-8353	14	9	the	the	DET
geusb-8353	14	10	properties	property	NOUN
geusb-8353	14	11	of	of	ADP
geusb-8353	14	12	the	the	DET
geusb-8353	14	13	subsurface	subsurface	NOUN
geusb-8353	14	14	are	be	AUX
geusb-8353	14	15	highly	highly	ADV
geusb-8353	14	16	non	non	ADJ
geusb-8353	14	17	-	-	ADJ
geusb-8353	14	18	stationary	stationary	ADJ
geusb-8353	14	19	,	,	PUNCT
geusb-8353	14	20	that	that	ADV
geusb-8353	14	21	is	is	ADV
geusb-8353	14	22	,	,	PUNCT
geusb-8353	14	23	they	they	PRON
geusb-8353	14	24	vary	vary	VERB
geusb-8353	14	25	significantly	significantly	ADV
geusb-8353	14	26	depending	depend	VERB
geusb-8353	14	27	on	on	ADP
geusb-8353	14	28	the	the	DET
geusb-8353	14	29	location	location	NOUN
geusb-8353	14	30	.	.	PUNCT
geusb-8353	15	1	consequently	consequently	ADV
geusb-8353	15	2	,	,	PUNCT
geusb-8353	15	3	in	in	ADP
geusb-8353	15	4	statistical	statistical	ADJ
geusb-8353	15	5	descriptions	description	NOUN
geusb-8353	15	6	of	of	ADP
geusb-8353	15	7	the	the	DET
geusb-8353	15	8	subsurface	subsurface	NOUN
geusb-8353	15	9	,	,	PUNCT
geusb-8353	15	10	there	there	PRON
geusb-8353	15	11	is	be	VERB
geusb-8353	15	12	a	a	DET
geusb-8353	15	13	need	need	NOUN
geusb-8353	15	14	for	for	ADP
geusb-8353	15	15	mapping	map	VERB
geusb-8353	15	16	non	non	ADJ
geusb-8353	15	17	-	-	NOUN
geusb-8353	15	18	stationarity	stationarity	NOUN
geusb-8353	15	19	in	in	ADP
geusb-8353	15	20	the	the	DET
geusb-8353	15	21	statistical	statistical	ADJ
geusb-8353	15	22	properties	property	NOUN
geusb-8353	15	23	and	and	CCONJ
geusb-8353	15	24	for	for	ADP
geusb-8353	15	25	practical	practical	ADJ
geusb-8353	15	26	purposes	purpose	NOUN
geusb-8353	15	27	also	also	ADV
geusb-8353	15	28	defining	define	VERB
geusb-8353	15	29	regions	region	NOUN
geusb-8353	15	30	wherein	wherein	ADJ
geusb-8353	15	31	local	local	ADJ
geusb-8353	15	32	stationarity	stationarity	NOUN
geusb-8353	15	33	can	can	AUX
geusb-8353	15	34	be	be	AUX
geusb-8353	15	35	assumed	assume	VERB
geusb-8353	15	36	(	(	PUNCT
geusb-8353	15	37	boisvert	boisvert	PROPN
geusb-8353	15	38	et	et	PROPN
geusb-8353	15	39	al	al	PROPN
geusb-8353	15	40	.	.	PROPN
geusb-8353	15	41	2009	2009	NUM
geusb-8353	15	42	)	)	PUNCT
geusb-8353	15	43	.	.	PUNCT
geusb-8353	16	1	estimating	estimate	VERB
geusb-8353	16	2	the	the	DET
geusb-8353	16	3	statistical	statistical	ADJ
geusb-8353	16	4	properties	property	NOUN
geusb-8353	16	5	of	of	ADP
geusb-8353	16	6	spatially	spatially	ADV
geusb-8353	16	7	distributed	distribute	VERB
geusb-8353	16	8	data	datum	NOUN
geusb-8353	16	9	is	be	AUX
geusb-8353	16	10	conventionally	conventionally	ADV
geusb-8353	16	11	done	do	VERB
geusb-8353	16	12	through	through	ADP
geusb-8353	16	13	semivariogram	semivariogram	NOUN
geusb-8353	16	14	analysis	analysis	NOUN
geusb-8353	16	15	,	,	PUNCT
geusb-8353	16	16	where	where	SCONJ
geusb-8353	16	17	an	an	DET
geusb-8353	16	18	experimental	experimental	ADJ
geusb-8353	16	19	semivariogram	semivariogram	NOUN
geusb-8353	16	20	is	be	AUX
geusb-8353	16	21	calculated	calculate	VERB
geusb-8353	16	22	as	as	ADP
geusb-8353	16	23	half	half	DET
geusb-8353	16	24	the	the	DET
geusb-8353	16	25	average	average	ADJ
geusb-8353	16	26	squared	squared	ADJ
geusb-8353	16	27	difference	difference	NOUN
geusb-8353	16	28	between	between	ADP
geusb-8353	16	29	points	point	NOUN
geusb-8353	16	30	separated	separate	VERB
geusb-8353	16	31	by	by	ADP
geusb-8353	16	32	some	some	DET
geusb-8353	16	33	distance	distance	NOUN
geusb-8353	16	34	,	,	PUNCT
geusb-8353	16	35	h	h	NOUN
geusb-8353	16	36	(	(	PUNCT
geusb-8353	16	37	matheron	matheron	PROPN
geusb-8353	16	38	1963	1963	NUM
geusb-8353	16	39	)	)	PUNCT
geusb-8353	16	40	.	.	PUNCT
geusb-8353	17	1	a	a	DET
geusb-8353	17	2	model	model	NOUN
geusb-8353	17	3	is	be	AUX
geusb-8353	17	4	fitted	fit	VERB
geusb-8353	17	5	to	to	ADP
geusb-8353	17	6	the	the	DET
geusb-8353	17	7	semivariogram	semivariogram	NOUN
geusb-8353	17	8	,	,	PUNCT
geusb-8353	17	9	which	which	PRON
geusb-8353	17	10	is	be	AUX
geusb-8353	17	11	typically	typically	ADV
geusb-8353	17	12	defined	define	VERB
geusb-8353	17	13	by	by	ADP
geusb-8353	17	14	two	two	NUM
geusb-8353	17	15	parameters	parameter	NOUN
geusb-8353	17	16	–	–	PUNCT
geusb-8353	17	17	a	a	DET
geusb-8353	17	18	range	range	NOUN
geusb-8353	17	19	and	and	CCONJ
geusb-8353	17	20	a	a	DET
geusb-8353	17	21	sill	sill	NOUN
geusb-8353	17	22	,	,	PUNCT
geusb-8353	17	23	and	and	CCONJ
geusb-8353	17	24	oftentimes	oftentime	VERB
geusb-8353	17	25	also	also	ADV
geusb-8353	17	26	a	a	DET
geusb-8353	17	27	nugget	nugget	ADJ
geusb-8353	17	28	effect	effect	NOUN
geusb-8353	17	29	(	(	PUNCT
geusb-8353	17	30	cressie	cressie	PROPN
geusb-8353	17	31	1993	1993	NUM
geusb-8353	17	32	)	)	PUNCT
geusb-8353	17	33	.	.	PUNCT
geusb-8353	18	1	alternatively	alternatively	ADV
geusb-8353	18	2	,	,	PUNCT
geusb-8353	18	3	it	it	PRON
geusb-8353	18	4	is	be	AUX
geusb-8353	18	5	possible	possible	ADJ
geusb-8353	18	6	to	to	PART
geusb-8353	18	7	estimate	estimate	VERB
geusb-8353	18	8	a	a	DET
geusb-8353	18	9	semivariogram	semivariogram	NOUN
geusb-8353	18	10	model	model	NOUN
geusb-8353	18	11	by	by	ADP
geusb-8353	18	12	determining	determine	VERB
geusb-8353	18	13	the	the	DET
geusb-8353	18	14	maximum	maximum	ADJ
geusb-8353	18	15	-	-	PUNCT
geusb-8353	18	16	likelihood	likelihood	NOUN
geusb-8353	18	17	combination	combination	NOUN
geusb-8353	18	18	of	of	ADP
geusb-8353	18	19	sill	sill	ADJ
geusb-8353	18	20	and	and	CCONJ
geusb-8353	18	21	range	range	NOUN
geusb-8353	18	22	given	give	VERB
geusb-8353	18	23	the	the	DET
geusb-8353	18	24	type	type	NOUN
geusb-8353	18	25	of	of	ADP
geusb-8353	18	26	model	model	NOUN
geusb-8353	18	27	.	.	PUNCT
geusb-8353	19	1	the	the	DET
geusb-8353	19	2	likelihood	likelihood	NOUN
geusb-8353	19	3	for	for	ADP
geusb-8353	19	4	a	a	DET
geusb-8353	19	5	semivariogram	semivariogram	NOUN
geusb-8353	19	6	model	model	NOUN
geusb-8353	19	7	is	be	AUX
geusb-8353	19	8	obtained	obtain	VERB
geusb-8353	19	9	by	by	ADP
geusb-8353	19	10	populating	populate	VERB
geusb-8353	19	11	a	a	DET
geusb-8353	19	12	covariance	covariance	NOUN
geusb-8353	19	13	matrix	matrix	NOUN
geusb-8353	19	14	using	use	VERB
geusb-8353	19	15	the	the	DET
geusb-8353	19	16	model	model	NOUN
geusb-8353	19	17	and	and	CCONJ
geusb-8353	19	18	taking	take	VERB
geusb-8353	19	19	the	the	DET
geusb-8353	19	20	probability	probability	NOUN
geusb-8353	19	21	density	density	NOUN
geusb-8353	19	22	of	of	ADP
geusb-8353	19	23	the	the	DET
geusb-8353	19	24	corresponding	correspond	VERB
geusb-8353	19	25	multivariate	multivariate	NOUN
geusb-8353	19	26	normal	normal	ADJ
geusb-8353	19	27	distribution	distribution	NOUN
geusb-8353	19	28	at	at	ADP
geusb-8353	19	29	the	the	DET
geusb-8353	19	30	point	point	NOUN
geusb-8353	19	31	defined	define	VERB
geusb-8353	19	32	by	by	ADP
geusb-8353	19	33	the	the	DET
geusb-8353	19	34	data	datum	NOUN
geusb-8353	19	35	(	(	PUNCT
geusb-8353	19	36	mardia	mardia	NOUN
geusb-8353	19	37	1990	1990	NUM
geusb-8353	19	38	)	)	PUNCT
geusb-8353	19	39	.	.	PUNCT
geusb-8353	20	1	fitting	fit	VERB
geusb-8353	20	2	a	a	DET
geusb-8353	20	3	semivariogram	semivariogram	NOUN
geusb-8353	20	4	and	and	CCONJ
geusb-8353	20	5	estimating	estimate	VERB
geusb-8353	20	6	the	the	DET
geusb-8353	20	7	maximum	maximum	ADJ
geusb-8353	20	8	-	-	PUNCT
geusb-8353	20	9	likelihood	likelihood	NOUN
geusb-8353	20	10	model	model	NOUN
geusb-8353	20	11	share	share	AUX
geusb-8353	20	12	the	the	DET
geusb-8353	20	13	drawbacks	drawback	NOUN
geusb-8353	20	14	of	of	ADP
geusb-8353	20	15	being	be	AUX
geusb-8353	20	16	computationally	computationally	ADV
geusb-8353	20	17	intensive	intensive	ADJ
geusb-8353	20	18	when	when	SCONJ
geusb-8353	20	19	many	many	ADJ
geusb-8353	20	20	models	model	NOUN
geusb-8353	20	21	need	need	VERB
geusb-8353	20	22	to	to	PART
geusb-8353	20	23	be	be	AUX
geusb-8353	20	24	estimated	estimate	VERB
geusb-8353	20	25	.	.	PUNCT
geusb-8353	21	1	we	we	PRON
geusb-8353	21	2	propose	propose	VERB
geusb-8353	21	3	a	a	DET
geusb-8353	21	4	new	new	ADJ
geusb-8353	21	5	method	method	NOUN
geusb-8353	21	6	of	of	ADP
geusb-8353	21	7	estimating	estimate	VERB
geusb-8353	21	8	the	the	DET
geusb-8353	21	9	sill	sill	ADJ
geusb-8353	21	10	and	and	CCONJ
geusb-8353	21	11	range	range	NOUN
geusb-8353	21	12	given	give	VERB
geusb-8353	21	13	a	a	DET
geusb-8353	21	14	set	set	NOUN
geusb-8353	21	15	of	of	ADP
geusb-8353	21	16	spatially	spatially	ADV
geusb-8353	21	17	distributed	distribute	VERB
geusb-8353	21	18	data	datum	NOUN
geusb-8353	21	19	using	use	VERB
geusb-8353	21	20	machine	machine	NOUN
geusb-8353	21	21	learning	learning	NOUN
geusb-8353	21	22	(	(	PUNCT
geusb-8353	21	23	ml	ml	NOUN
geusb-8353	21	24	)	)	PUNCT
geusb-8353	21	25	,	,	PUNCT
geusb-8353	21	26	specifically	specifically	ADV
geusb-8353	21	27	a	a	DET
geusb-8353	21	28	convolutional	convolutional	ADJ
geusb-8353	21	29	neural	neural	ADJ
geusb-8353	21	30	network	network	NOUN
geusb-8353	21	31	(	(	PUNCT
geusb-8353	21	32	cnn	cnn	PROPN
geusb-8353	21	33	)	)	PUNCT
geusb-8353	21	34	,	,	PUNCT
geusb-8353	21	35	which	which	PRON
geusb-8353	21	36	is	be	AUX
geusb-8353	21	37	more	more	ADV
geusb-8353	21	38	efficient	efficient	ADJ
geusb-8353	21	39	when	when	SCONJ
geusb-8353	21	40	estimating	estimate	VERB
geusb-8353	21	41	many	many	ADJ
geusb-8353	21	42	models	model	NOUN
geusb-8353	21	43	.	.	PUNCT
geusb-8353	22	1	a	a	DET
geusb-8353	22	2	cnn	cnn	PROPN
geusb-8353	22	3	is	be	AUX
geusb-8353	22	4	trained	train	VERB
geusb-8353	22	5	to	to	PART
geusb-8353	22	6	recognise	recognise	VERB
geusb-8353	22	7	the	the	DET
geusb-8353	22	8	approximate	approximate	ADJ
geusb-8353	22	9	mapping	mapping	NOUN
geusb-8353	22	10	from	from	ADP
geusb-8353	22	11	a	a	DET
geusb-8353	22	12	realisation	realisation	NOUN
geusb-8353	22	13	of	of	ADP
geusb-8353	22	14	a	a	DET
geusb-8353	22	15	gaussian	gaussian	ADJ
geusb-8353	22	16	random	random	ADJ
geusb-8353	22	17	field	field	NOUN
geusb-8353	22	18	to	to	ADP
geusb-8353	22	19	the	the	DET
geusb-8353	22	20	semivariogram	semivariogram	NOUN
geusb-8353	22	21	model	model	NOUN
geusb-8353	22	22	that	that	PRON
geusb-8353	22	23	defines	define	VERB
geusb-8353	22	24	its	its	PRON
geusb-8353	22	25	probability	probability	NOUN
geusb-8353	22	26	density	density	NOUN
geusb-8353	22	27	function	function	NOUN
geusb-8353	22	28	.	.	PUNCT
geusb-8353	23	1	this	this	DET
geusb-8353	23	2	mapping	mapping	NOUN
geusb-8353	23	3	is	be	AUX
geusb-8353	23	4	not	not	PART
geusb-8353	23	5	bijective	bijective	ADJ
geusb-8353	23	6	in	in	ADP
geusb-8353	23	7	nature	nature	NOUN
geusb-8353	23	8	,	,	PUNCT
geusb-8353	23	9	and	and	CCONJ
geusb-8353	23	10	as	as	ADP
geusb-8353	23	11	such	such	ADJ
geusb-8353	23	12	,	,	PUNCT
geusb-8353	23	13	it	it	PRON
geusb-8353	23	14	is	be	AUX
geusb-8353	23	15	not	not	PART
geusb-8353	23	16	a	a	DET
geusb-8353	23	17	function	function	NOUN
geusb-8353	23	18	.	.	PUNCT
geusb-8353	24	1	however	however	ADV
geusb-8353	24	2	,	,	PUNCT
geusb-8353	24	3	the	the	DET
geusb-8353	24	4	network	network	NOUN
geusb-8353	24	5	should	should	AUX
geusb-8353	24	6	still	still	ADV
geusb-8353	24	7	be	be	AUX
geusb-8353	24	8	able	able	ADJ
geusb-8353	24	9	to	to	PART
geusb-8353	24	10	approximate	approximate	VERB
geusb-8353	24	11	a	a	DET
geusb-8353	24	12	function	function	NOUN
geusb-8353	24	13	that	that	PRON
geusb-8353	24	14	resembles	resemble	VERB
geusb-8353	24	15	the	the	DET
geusb-8353	24	16	mapping	mapping	NOUN
geusb-8353	24	17	to	to	ADP
geusb-8353	24	18	some	some	DET
geusb-8353	24	19	degree	degree	NOUN
geusb-8353	24	20	.	.	PUNCT
geusb-8353	25	1	the	the	DET
geusb-8353	25	2	idea	idea	NOUN
geusb-8353	25	3	of	of	ADP
geusb-8353	25	4	using	use	VERB
geusb-8353	25	5	a	a	DET
geusb-8353	25	6	cnn	cnn	NOUN
geusb-8353	25	7	for	for	ADP
geusb-8353	25	8	semivariogram	semivariogram	NOUN
geusb-8353	25	9	modelling	modelling	NOUN
geusb-8353	25	10	has	have	AUX
geusb-8353	25	11	been	be	AUX
geusb-8353	25	12	proposed	propose	VERB
geusb-8353	25	13	before	before	ADV
geusb-8353	25	14	.	.	PUNCT
geusb-8353	26	1	one	one	NUM
geusb-8353	26	2	study	study	NOUN
geusb-8353	26	3	used	use	VERB
geusb-8353	26	4	separate	separate	ADJ
geusb-8353	26	5	networks	network	NOUN
geusb-8353	26	6	for	for	ADP
geusb-8353	26	7	interpolation	interpolation	NOUN
geusb-8353	26	8	and	and	CCONJ
geusb-8353	26	9	parameter	parameter	NOUN
geusb-8353	26	10	estimation	estimation	NOUN
geusb-8353	26	11	(	(	PUNCT
geusb-8353	26	12	jo	jo	PROPN
geusb-8353	26	13	&	&	CCONJ
geusb-8353	26	14	pyrcz	pyrcz	NOUN
geusb-8353	26	15	2022	2022	NUM
geusb-8353	26	16	)	)	PUNCT
geusb-8353	26	17	.	.	PUNCT
geusb-8353	27	1	others	other	NOUN
geusb-8353	27	2	skipped	skip	VERB
geusb-8353	27	3	parameterised	parameterised	ADJ
geusb-8353	27	4	models	model	NOUN
geusb-8353	27	5	and	and	CCONJ
geusb-8353	27	6	directly	directly	ADV
geusb-8353	27	7	estimated	estimate	VERB
geusb-8353	27	8	semivariograms	semivariogram	NOUN
geusb-8353	27	9	for	for	ADP
geusb-8353	27	10	kriging	krige	VERB
geusb-8353	27	11	(	(	PUNCT
geusb-8353	27	12	li	li	PROPN
geusb-8353	27	13	et	et	PROPN
geusb-8353	27	14	  	  	SPACE
geusb-8353	27	15	al	al	PROPN
geusb-8353	27	16	.	.	PROPN
geusb-8353	27	17	2022	2022	NUM
geusb-8353	27	18	)	)	PUNCT
geusb-8353	27	19	.	.	PUNCT
geusb-8353	28	1	we	we	PRON
geusb-8353	28	2	have	have	AUX
geusb-8353	28	3	chosen	choose	VERB
geusb-8353	28	4	to	to	PART
geusb-8353	28	5	focus	focus	VERB
geusb-8353	28	6	on	on	ADP
geusb-8353	28	7	estimating	estimate	VERB
geusb-8353	28	8	gaussian	gaussian	ADJ
geusb-8353	28	9	model	model	NOUN
geusb-8353	28	10	parameters	parameter	NOUN
geusb-8353	28	11	using	use	VERB
geusb-8353	28	12	one	one	NUM
geusb-8353	28	13	network	network	NOUN
geusb-8353	28	14	to	to	PART
geusb-8353	28	15	infer	infer	VERB
geusb-8353	28	16	their	their	PRON
geusb-8353	28	17	spatial	spatial	ADJ
geusb-8353	28	18	distribution	distribution	NOUN
geusb-8353	28	19	within	within	ADP
geusb-8353	28	20	large	large	ADJ
geusb-8353	28	21	models	model	NOUN
geusb-8353	28	22	and	and	CCONJ
geusb-8353	28	23	make	make	VERB
geusb-8353	28	24	the	the	DET
geusb-8353	28	25	process	process	NOUN
geusb-8353	28	26	computationally	computationally	ADV
geusb-8353	28	27	feasible	feasible	ADJ
geusb-8353	28	28	.	.	PUNCT
geusb-8353	29	1	we	we	PRON
geusb-8353	29	2	test	test	VERB
geusb-8353	29	3	the	the	DET
geusb-8353	29	4	method	method	NOUN
geusb-8353	29	5	on	on	ADP
geusb-8353	29	6	the	the	DET
geusb-8353	29	7	danish	danish	ADJ
geusb-8353	29	8	national	national	ADJ
geusb-8353	29	9	hydrostratigraphic	hydrostratigraphic	ADJ
geusb-8353	29	10	model	model	NOUN
geusb-8353	29	11	(	(	PUNCT
geusb-8353	29	12	dk	dk	NOUN
geusb-8353	29	13	-	-	NOUN
geusb-8353	29	14	model	model	NOUN
geusb-8353	29	15	;	;	PUNCT
geusb-8353	29	16	stisen	stisen	PROPN
geusb-8353	29	17	et	et	PROPN
geusb-8353	29	18	al	al	PROPN
geusb-8353	29	19	.	.	PROPN
geusb-8353	29	20	2020	2020	NUM
geusb-8353	29	21	)	)	PUNCT
geusb-8353	29	22	.	.	PUNCT
geusb-8353	30	1	we	we	PRON
geusb-8353	30	2	show	show	VERB
geusb-8353	30	3	that	that	SCONJ
geusb-8353	30	4	where	where	SCONJ
geusb-8353	30	5	local	local	ADJ
geusb-8353	30	6	stationarity	stationarity	NOUN
geusb-8353	30	7	may	may	AUX
geusb-8353	30	8	be	be	AUX
geusb-8353	30	9	assumed	assume	VERB
geusb-8353	30	10	,	,	PUNCT
geusb-8353	30	11	we	we	PRON
geusb-8353	30	12	can	can	AUX
geusb-8353	30	13	define	define	VERB
geusb-8353	30	14	regions	region	NOUN
geusb-8353	30	15	within	within	ADP
geusb-8353	30	16	the	the	DET
geusb-8353	30	17	hydrostratigraphic	hydrostratigraphic	ADJ
geusb-8353	30	18	model	model	NOUN
geusb-8353	30	19	with	with	ADP
geusb-8353	30	20	reasonable	reasonable	ADJ
geusb-8353	30	21	accuracy	accuracy	NOUN
geusb-8353	30	22	by	by	ADP
geusb-8353	30	23	clustering	cluster	VERB
geusb-8353	30	24	the	the	DET
geusb-8353	30	25	models	model	NOUN
geusb-8353	30	26	.	.	PUNCT
geusb-8353	31	1	required	require	VERB
geusb-8353	31	2	resources	resource	NOUN
geusb-8353	31	3	the	the	DET
geusb-8353	31	4	required	require	VERB
geusb-8353	31	5	resources	resource	NOUN
geusb-8353	31	6	are	be	AUX
geusb-8353	31	7	as	as	SCONJ
geusb-8353	31	8	follows	follow	VERB
geusb-8353	31	9	:	:	PUNCT
geusb-8353	31	10	•	•	NOUN
geusb-8353	31	11	 	 	SPACE
geusb-8353	31	12	a	a	DET
geusb-8353	31	13	sufficiently	sufficiently	ADV
geusb-8353	31	14	effective	effective	ADJ
geusb-8353	31	15	computer	computer	NOUN
geusb-8353	31	16	•	•	NOUN
geusb-8353	31	17	  	  	SPACE
geusb-8353	31	18	matlab	matlab	PROPN
geusb-8353	31	19	®	®	NOUN
geusb-8353	31	20	2022b	2022b	NUM
geusb-8353	31	21	or	or	CCONJ
geusb-8353	31	22	newer	new	ADJ
geusb-8353	31	23	–	–	PUNCT
geusb-8353	31	24	older	old	ADJ
geusb-8353	31	25	versions	version	NOUN
geusb-8353	31	26	have	have	AUX
geusb-8353	31	27	not	not	PART
geusb-8353	31	28	been	be	AUX
geusb-8353	31	29	tested	test	VERB
geusb-8353	31	30	for	for	ADP
geusb-8353	31	31	this	this	DET
geusb-8353	31	32	method	method	NOUN
geusb-8353	31	33	.	.	PUNCT
geusb-8353	32	1	also	also	ADV
geusb-8353	32	2	,	,	PUNCT
geusb-8353	32	3	the	the	DET
geusb-8353	32	4	machine	machine	NOUN
geusb-8353	32	5	learning	learn	VERB
geusb-8353	32	6	toolbox	toolbox	NOUN
geusb-8353	32	7	for	for	ADP
geusb-8353	32	8	matlab	matlab	PROPN
geusb-8353	32	9	,	,	PUNCT
geusb-8353	32	10	sippi	sippi	NOUN
geusb-8353	32	11	geostatistics	geostatistics	PROPN
geusb-8353	32	12	toolbox	toolbox	NOUN
geusb-8353	32	13	for	for	ADP
geusb-8353	32	14	matlab	matlab	PROPN
geusb-8353	32	15	and	and	CCONJ
geusb-8353	32	16	the	the	DET
geusb-8353	32	17	mgstat	mgstat	NOUN
geusb-8353	32	18	geostatistics	geostatistics	PROPN
geusb-8353	32	19	toolbox	toolbox	NOUN
geusb-8353	32	20	for	for	ADP
geusb-8353	32	21	matlab	matlab	PROPN
geusb-8353	32	22	•	•	PUNCT
geusb-8353	32	23	  	  	SPACE
geusb-8353	32	24	data	datum	NOUN
geusb-8353	32	25	in	in	ADP
geusb-8353	32	26	the	the	DET
geusb-8353	32	27	form	form	NOUN
geusb-8353	32	28	of	of	ADP
geusb-8353	32	29	scattered	scatter	VERB
geusb-8353	32	30	points	point	NOUN
geusb-8353	32	31	with	with	ADP
geusb-8353	32	32	a	a	DET
geusb-8353	32	33	value	value	NOUN
geusb-8353	32	34	for	for	ADP
geusb-8353	32	35	each	each	DET
geusb-8353	32	36	point	point	NOUN
geusb-8353	32	37	,	,	PUNCT
geusb-8353	32	38	either	either	CCONJ
geusb-8353	32	39	with	with	ADP
geusb-8353	32	40	irregular	irregular	ADJ
geusb-8353	32	41	spacing	spacing	NOUN
geusb-8353	32	42	or	or	CCONJ
geusb-8353	32	43	as	as	ADP
geusb-8353	32	44	a	a	DET
geusb-8353	32	45	regular	regular	ADJ
geusb-8353	32	46	grid	grid	ADJ
geusb-8353	32	47	methodological	methodological	ADJ
geusb-8353	32	48	protocols	protocol	NOUN
geusb-8353	32	49	we	we	PRON
geusb-8353	32	50	use	use	VERB
geusb-8353	32	51	the	the	DET
geusb-8353	32	52	cnn	cnn	PROPN
geusb-8353	32	53	’s	’s	PART
geusb-8353	32	54	ability	ability	NOUN
geusb-8353	32	55	to	to	PART
geusb-8353	32	56	efficiently	efficiently	ADV
geusb-8353	32	57	detect	detect	VERB
geusb-8353	32	58	structural	structural	ADJ
geusb-8353	32	59	patterns	pattern	NOUN
geusb-8353	32	60	in	in	ADP
geusb-8353	32	61	an	an	DET
geusb-8353	32	62	image	image	NOUN
geusb-8353	32	63	,	,	PUNCT
geusb-8353	32	64	and	and	CCONJ
geusb-8353	32	65	as	as	ADP
geusb-8353	32	66	such	such	ADJ
geusb-8353	32	67	,	,	PUNCT
geusb-8353	32	68	it	it	PRON
geusb-8353	32	69	needs	need	VERB
geusb-8353	32	70	a	a	DET
geusb-8353	32	71	regular	regular	ADJ
geusb-8353	32	72	grid	grid	NOUN
geusb-8353	32	73	as	as	ADP
geusb-8353	32	74	input	input	NOUN
geusb-8353	32	75	.	.	PUNCT
geusb-8353	33	1	we	we	PRON
geusb-8353	33	2	consider	consider	VERB
geusb-8353	33	3	two	two	NUM
geusb-8353	33	4	cases	case	NOUN
geusb-8353	33	5	:	:	PUNCT
geusb-8353	33	6	one	one	NUM
geusb-8353	33	7	where	where	SCONJ
geusb-8353	33	8	data	datum	NOUN
geusb-8353	33	9	constitute	constitute	VERB
geusb-8353	33	10	a	a	DET
geusb-8353	33	11	full	full	ADJ
geusb-8353	33	12	grid	grid	NOUN
geusb-8353	33	13	and	and	CCONJ
geusb-8353	33	14	one	one	NUM
geusb-8353	33	15	with	with	ADP
geusb-8353	33	16	scattered	scatter	VERB
geusb-8353	33	17	point	point	NOUN
geusb-8353	33	18	data	datum	NOUN
geusb-8353	33	19	interpolated	interpolate	VERB
geusb-8353	33	20	to	to	PART
geusb-8353	33	21	produce	produce	VERB
geusb-8353	33	22	a	a	DET
geusb-8353	33	23	grid	grid	NOUN
geusb-8353	33	24	.	.	PUNCT
geusb-8353	34	1	for	for	ADP
geusb-8353	34	2	the	the	DET
geusb-8353	34	3	network	network	NOUN
geusb-8353	34	4	input	input	NOUN
geusb-8353	34	5	,	,	PUNCT
geusb-8353	34	6	we	we	PRON
geusb-8353	34	7	chose	choose	VERB
geusb-8353	34	8	the	the	DET
geusb-8353	34	9	grid	grid	NOUN
geusb-8353	34	10	size	size	NOUN
geusb-8353	34	11	31	31	NUM
geusb-8353	34	12	×	×	NOUN
geusb-8353	34	13	31	31	NUM
geusb-8353	34	14	cells	cell	NOUN
geusb-8353	34	15	with	with	ADP
geusb-8353	34	16	a	a	DET
geusb-8353	34	17	cell	cell	NOUN
geusb-8353	34	18	size	size	NOUN
geusb-8353	34	19	of	of	ADP
geusb-8353	34	20	100	100	NUM
geusb-8353	34	21	m	m	NOUN
geusb-8353	34	22	,	,	PUNCT
geusb-8353	34	23	and	and	CCONJ
geusb-8353	34	24	we	we	PRON
geusb-8353	34	25	adapted	adapt	VERB
geusb-8353	34	26	the	the	DET
geusb-8353	34	27	neural	neural	ADJ
geusb-8353	34	28	network	network	NOUN
geusb-8353	34	29	(	(	PUNCT
geusb-8353	34	30	nn	nn	NOUN
geusb-8353	34	31	)	)	PUNCT
geusb-8353	34	32	architecture	architecture	NOUN
geusb-8353	34	33	from	from	ADP
geusb-8353	34	34	the	the	DET
geusb-8353	34	35	squeezenet	squeezenet	NOUN
geusb-8353	34	36	convolutional	convolutional	ADJ
geusb-8353	34	37	neural	neural	ADJ
geusb-8353	34	38	network	network	NOUN
geusb-8353	34	39	(	(	PUNCT
geusb-8353	34	40	iandola	iandola	PROPN
geusb-8353	34	41	et	et	PROPN
geusb-8353	34	42	al	al	PROPN
geusb-8353	34	43	.	.	PROPN
geusb-8353	34	44	2016	2016	NUM
geusb-8353	34	45	)	)	PUNCT
geusb-8353	34	46	,	,	PUNCT
geusb-8353	34	47	which	which	PRON
geusb-8353	34	48	is	be	AUX
geusb-8353	34	49	a	a	DET
geusb-8353	34	50	native	native	ADJ
geusb-8353	34	51	architecture	architecture	NOUN
geusb-8353	34	52	in	in	ADP
geusb-8353	34	53	the	the	DET
geusb-8353	34	54	matlab	matlab	PROPN
geusb-8353	34	55	machine	machine	NOUN
geusb-8353	34	56	learning	learning	NOUN
geusb-8353	34	57	toolbox	toolbox	NOUN
geusb-8353	34	58	.	.	PUNCT
geusb-8353	35	1	producing	produce	VERB
geusb-8353	35	2	training	training	NOUN
geusb-8353	35	3	,	,	PUNCT
geusb-8353	35	4	validation	validation	NOUN
geusb-8353	35	5	and	and	CCONJ
geusb-8353	35	6	test	test	NOUN
geusb-8353	35	7	data	datum	NOUN
geusb-8353	35	8	we	we	PRON
geusb-8353	35	9	produced	produce	VERB
geusb-8353	35	10	synthetic	synthetic	ADJ
geusb-8353	35	11	data	datum	NOUN
geusb-8353	35	12	for	for	ADP
geusb-8353	35	13	the	the	DET
geusb-8353	35	14	nn	nn	NOUN
geusb-8353	35	15	using	use	VERB
geusb-8353	35	16	the	the	DET
geusb-8353	35	17	sippi	sippi	NOUN
geusb-8353	35	18	toolbox	toolbox	NOUN
geusb-8353	35	19	in	in	ADP
geusb-8353	35	20	matlab	matlab	PROPN
geusb-8353	35	21	(	(	PUNCT
geusb-8353	35	22	hansen	hansen	PROPN
geusb-8353	35	23	et	et	PROPN
geusb-8353	35	24	al	al	PROPN
geusb-8353	35	25	.	.	PROPN
geusb-8353	35	26	2013	2013	NUM
geusb-8353	35	27	)	)	PUNCT
geusb-8353	35	28	by	by	ADP
geusb-8353	35	29	taking	take	VERB
geusb-8353	35	30	150	150	NUM
geusb-8353	35	31	 	 	SPACE
geusb-8353	35	32	000	000	NUM
geusb-8353	35	33	values	value	NOUN
geusb-8353	35	34	for	for	ADP
geusb-8353	35	35	sill	sill	ADJ
geusb-8353	35	36	and	and	CCONJ
geusb-8353	35	37	range	range	VERB
geusb-8353	35	38	from	from	ADP
geusb-8353	35	39	uniform	uniform	ADJ
geusb-8353	35	40	distributions	distribution	NOUN
geusb-8353	35	41	,	,	PUNCT
geusb-8353	35	42	such	such	ADJ
geusb-8353	35	43	that	that	SCONJ
geusb-8353	35	44	the	the	DET
geusb-8353	35	45	sills	sill	NOUN
geusb-8353	35	46	vary	vary	VERB
geusb-8353	35	47	between	between	ADP
geusb-8353	35	48	0	0	NUM
geusb-8353	35	49	m2	m2	PROPN
geusb-8353	35	50	and	and	CCONJ
geusb-8353	35	51	1100	1100	NUM
geusb-8353	35	52	m2	m2	PROPN
geusb-8353	35	53	,	,	PUNCT
geusb-8353	35	54	and	and	CCONJ
geusb-8353	35	55	the	the	DET
geusb-8353	35	56	ranges	range	NOUN
geusb-8353	35	57	vary	vary	VERB
geusb-8353	35	58	between	between	ADP
geusb-8353	35	59	100	100	NUM
geusb-8353	35	60	m	m	NOUN
geusb-8353	35	61	and	and	CCONJ
geusb-8353	35	62	3000	3000	NUM
geusb-8353	35	63	m.	m.	NOUN
geusb-8353	35	64	we	we	PRON
geusb-8353	35	65	then	then	ADV
geusb-8353	35	66	simulate	simulate	VERB
geusb-8353	35	67	a	a	DET
geusb-8353	35	68	realisation	realisation	NOUN
geusb-8353	35	69	from	from	ADP
geusb-8353	35	70	each	each	PRON
geusb-8353	35	71	of	of	ADP
geusb-8353	35	72	the	the	DET
geusb-8353	35	73	gaussian	gaussian	ADJ
geusb-8353	35	74	random	random	ADJ
geusb-8353	35	75	fields	field	NOUN
geusb-8353	35	76	that	that	PRON
geusb-8353	35	77	have	have	VERB
geusb-8353	35	78	the	the	DET
geusb-8353	35	79	gaussian	gaussian	ADJ
geusb-8353	35	80	semivariance	semivariance	NOUN
geusb-8353	35	81	functions	function	NOUN
geusb-8353	35	82	defined	define	VERB
geusb-8353	35	83	by	by	ADP
geusb-8353	35	84	the	the	DET
geusb-8353	35	85	pairs	pair	NOUN
geusb-8353	35	86	of	of	ADP
geusb-8353	35	87	sill	sill	ADJ
geusb-8353	35	88	and	and	CCONJ
geusb-8353	35	89	range	range	NOUN
geusb-8353	35	90	values	value	NOUN
geusb-8353	35	91	.	.	PUNCT
geusb-8353	36	1	each	each	DET
geusb-8353	36	2	realisation	realisation	NOUN
geusb-8353	36	3	is	be	AUX
geusb-8353	36	4	on	on	ADP
geusb-8353	36	5	the	the	DET
geusb-8353	36	6	31	31	NUM
geusb-8353	36	7	×	×	NOUN
geusb-8353	36	8	31	31	NUM
geusb-8353	36	9	grid	grid	NOUN
geusb-8353	36	10	with	with	ADP
geusb-8353	36	11	a	a	DET
geusb-8353	36	12	cell	cell	NOUN
geusb-8353	36	13	size	size	NOUN
geusb-8353	36	14	of	of	ADP
geusb-8353	36	15	100	100	NUM
geusb-8353	36	16	m.	m.	NOUN
geusb-8353	36	17	to	to	PART
geusb-8353	36	18	include	include	VERB
geusb-8353	36	19	some	some	DET
geusb-8353	36	20	component	component	NOUN
geusb-8353	36	21	of	of	ADP
geusb-8353	36	22	noise	noise	NOUN
geusb-8353	36	23	,	,	PUNCT
geusb-8353	36	24	we	we	PRON
geusb-8353	36	25	simulated	simulate	VERB
geusb-8353	36	26	and	and	CCONJ
geusb-8353	36	27	added	add	VERB
geusb-8353	36	28	one	one	NUM
geusb-8353	36	29	more	more	ADJ
geusb-8353	36	30	realisation	realisation	NOUN
geusb-8353	36	31	to	to	ADP
geusb-8353	36	32	each	each	DET
geusb-8353	36	33	existing	exist	VERB
geusb-8353	36	34	realisation	realisation	NOUN
geusb-8353	36	35	,	,	PUNCT
geusb-8353	36	36	with	with	ADP
geusb-8353	36	37	the	the	DET
geusb-8353	36	38	same	same	ADJ
geusb-8353	36	39	effective	effective	ADJ
geusb-8353	36	40	range	range	NOUN
geusb-8353	36	41	,	,	PUNCT
geusb-8353	36	42	and	and	CCONJ
geusb-8353	36	43	the	the	DET
geusb-8353	36	44	sill	sill	NOUN
geusb-8353	36	45	being	be	AUX
geusb-8353	36	46	random	random	ADJ
geusb-8353	36	47	between	between	ADP
geusb-8353	36	48	0	0	NUM
geusb-8353	36	49	 	 	SPACE
geusb-8353	36	50	m2	m2	PROPN
geusb-8353	36	51	and	and	CCONJ
geusb-8353	36	52	the	the	DET
geusb-8353	36	53	sill	sill	NOUN
geusb-8353	36	54	of	of	ADP
geusb-8353	36	55	the	the	DET
geusb-8353	36	56	original	original	ADJ
geusb-8353	36	57	realisation	realisation	NOUN
geusb-8353	36	58	itself	itself	PRON
geusb-8353	36	59	.	.	PUNCT
geusb-8353	37	1	figure	figure	VERB
geusb-8353	37	2	1	1	NUM
geusb-8353	37	3	shows	show	VERB
geusb-8353	37	4	nine	nine	NUM
geusb-8353	37	5	examples	example	NOUN
geusb-8353	37	6	of	of	ADP
geusb-8353	37	7	the	the	DET
geusb-8353	37	8	synthetic	synthetic	ADJ
geusb-8353	37	9	data	datum	NOUN
geusb-8353	37	10	.	.	PUNCT
geusb-8353	38	1	the	the	DET
geusb-8353	38	2	synthetic	synthetic	ADJ
geusb-8353	38	3	data	datum	NOUN
geusb-8353	38	4	are	be	AUX
geusb-8353	38	5	saved	save	VERB
geusb-8353	38	6	both	both	CCONJ
geusb-8353	38	7	as	as	ADP
geusb-8353	38	8	full	full	ADJ
geusb-8353	38	9	grids	grid	NOUN
geusb-8353	38	10	and	and	CCONJ
geusb-8353	38	11	sets	set	NOUN
geusb-8353	38	12	of	of	ADP
geusb-8353	38	13	120	120	NUM
geusb-8353	38	14	points	point	NOUN
geusb-8353	38	15	with	with	ADP
geusb-8353	38	16	an	an	DET
geusb-8353	38	17	x	x	NOUN
geusb-8353	38	18	-	-	NOUN
geusb-8353	38	19	coordinate	coordinate	NOUN
geusb-8353	38	20	,	,	PUNCT
geusb-8353	38	21	a	a	DET
geusb-8353	38	22	y	y	NOUN
geusb-8353	38	23	-	-	PUNCT
geusb-8353	38	24	coordinate	coordinate	NOUN
geusb-8353	38	25	and	and	CCONJ
geusb-8353	38	26	a	a	DET
geusb-8353	38	27	z	z	NOUN
geusb-8353	38	28	-	-	PUNCT
geusb-8353	38	29	coordinate	coordinate	NOUN
geusb-8353	38	30	.	.	PUNCT
geusb-8353	39	1	we	we	PRON
geusb-8353	39	2	split	split	VERB
geusb-8353	39	3	the	the	DET
geusb-8353	39	4	synthetic	synthetic	ADJ
geusb-8353	39	5	data	datum	NOUN
geusb-8353	39	6	into	into	ADP
geusb-8353	39	7	a	a	DET
geusb-8353	39	8	training	training	NOUN
geusb-8353	39	9	set	set	NOUN
geusb-8353	39	10	,	,	PUNCT
geusb-8353	39	11	consisting	consist	VERB
geusb-8353	39	12	of	of	ADP
geusb-8353	39	13	90	90	NUM
geusb-8353	39	14	%	%	NOUN
geusb-8353	39	15	of	of	ADP
geusb-8353	39	16	the	the	DET
geusb-8353	39	17	data	datum	NOUN
geusb-8353	39	18	,	,	PUNCT
geusb-8353	39	19	as	as	ADV
geusb-8353	39	20	well	well	ADV
geusb-8353	39	21	as	as	ADP
geusb-8353	39	22	a	a	DET
geusb-8353	39	23	validation	validation	NOUN
geusb-8353	39	24	and	and	CCONJ
geusb-8353	39	25	a	a	DET
geusb-8353	39	26	training	training	NOUN
geusb-8353	39	27	set	set	NOUN
geusb-8353	39	28	,	,	PUNCT
geusb-8353	39	29	each	each	PRON
geusb-8353	39	30	being	be	AUX
geusb-8353	39	31	5	5	NUM
geusb-8353	39	32	%	%	NOUN
geusb-8353	39	33	of	of	ADP
geusb-8353	39	34	the	the	DET
geusb-8353	39	35	data	datum	NOUN
geusb-8353	39	36	.	.	PUNCT
geusb-8353	40	1	training	train	VERB
geusb-8353	40	2	the	the	DET
geusb-8353	40	3	network	network	NOUN
geusb-8353	40	4	the	the	DET
geusb-8353	40	5	network	network	NOUN
geusb-8353	40	6	is	be	AUX
geusb-8353	40	7	trained	train	VERB
geusb-8353	40	8	with	with	ADP
geusb-8353	40	9	the	the	DET
geusb-8353	40	10	‘	'	PUNCT
geusb-8353	40	11	adam	adam	ADJ
geusb-8353	40	12	’	'	PUNCT
geusb-8353	40	13	optimisation	optimisation	NOUN
geusb-8353	40	14	algorithm	algorithm	NOUN
geusb-8353	40	15	,	,	PUNCT
geusb-8353	40	16	and	and	CCONJ
geusb-8353	40	17	the	the	DET
geusb-8353	40	18	loss	loss	NOUN
geusb-8353	40	19	function	function	NOUN
geusb-8353	40	20	is	be	AUX
geusb-8353	40	21	represented	represent	VERB
geusb-8353	40	22	by	by	ADP
geusb-8353	40	23	the	the	DET
geusb-8353	40	24	mean	mean	ADJ
geusb-8353	40	25	squared	square	VERB
geusb-8353	40	26	error	error	NOUN
geusb-8353	40	27	.	.	PUNCT
geusb-8353	41	1	we	we	PRON
geusb-8353	41	2	used	use	VERB
geusb-8353	41	3	a	a	DET
geusb-8353	41	4	constant	constant	ADJ
geusb-8353	41	5	learning	learning	NOUN
geusb-8353	41	6	rate	rate	NOUN
geusb-8353	41	7	of	of	ADP
geusb-8353	41	8	0.0005	0.0005	NUM
geusb-8353	41	9	and	and	CCONJ
geusb-8353	41	10	a	a	DET
geusb-8353	41	11	batch	batch	NOUN
geusb-8353	41	12	size	size	NOUN
geusb-8353	41	13	of	of	ADP
geusb-8353	41	14	1000	1000	NUM
geusb-8353	41	15	whilst	whilst	SCONJ
geusb-8353	41	16	training	train	VERB
geusb-8353	41	17	the	the	DET
geusb-8353	41	18	model	model	NOUN
geusb-8353	41	19	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	41	20	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	41	21	falk	falk	NOUN
geusb-8353	41	22	&	&	CCONJ
geusb-8353	41	23	madsen	madsen	PROPN
geusb-8353	41	24	2023	2023	NUM
geusb-8353	41	25	:	:	PUNCT
geusb-8353	41	26	geus	geus	NOUN
geusb-8353	41	27	bulletin	bulletin	NOUN
geusb-8353	41	28	53	53	NUM
geusb-8353	41	29	.	.	PUNCT
geusb-8353	41	30	8353	8353	NUM
geusb-8353	41	31	.	.	PUNCT
geusb-8353	42	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	42	2	3	3	NUM
geusb-8353	42	3	of	of	ADP
geusb-8353	42	4	7	7	NUM
geusb-8353	42	5	www.geusbul	www.geusbul	NOUN
geusb-8353	42	6	let	let	VERB
geusb-8353	42	7	in.org	in.org	ADJ
geusb-8353	42	8	for	for	ADP
geusb-8353	42	9	100	100	NUM
geusb-8353	42	10	epochs	epoch	NOUN
geusb-8353	42	11	.	.	PUNCT
geusb-8353	43	1	the	the	DET
geusb-8353	43	2	total	total	ADJ
geusb-8353	43	3	training	training	NOUN
geusb-8353	43	4	time	time	NOUN
geusb-8353	43	5	for	for	ADP
geusb-8353	43	6	the	the	DET
geusb-8353	43	7	network	network	NOUN
geusb-8353	43	8	is	be	AUX
geusb-8353	43	9	about	about	ADV
geusb-8353	43	10	1	1	NUM
geusb-8353	43	11	h.	h.	NOUN
geusb-8353	43	12	we	we	PRON
geusb-8353	43	13	also	also	ADV
geusb-8353	43	14	found	find	VERB
geusb-8353	43	15	that	that	SCONJ
geusb-8353	43	16	model	model	NOUN
geusb-8353	43	17	training	training	NOUN
geusb-8353	43	18	time	time	NOUN
geusb-8353	43	19	could	could	AUX
geusb-8353	43	20	be	be	AUX
geusb-8353	43	21	reduced	reduce	VERB
geusb-8353	43	22	to	to	ADP
geusb-8353	43	23	only	only	ADV
geusb-8353	43	24	a	a	DET
geusb-8353	43	25	few	few	ADJ
geusb-8353	43	26	minutes	minute	NOUN
geusb-8353	43	27	if	if	SCONJ
geusb-8353	43	28	we	we	PRON
geusb-8353	43	29	included	include	VERB
geusb-8353	43	30	the	the	DET
geusb-8353	43	31	fast	fast	ADJ
geusb-8353	43	32	fourier	fourier	NOUN
geusb-8353	43	33	transform	transform	NOUN
geusb-8353	43	34	as	as	ADP
geusb-8353	43	35	a	a	DET
geusb-8353	43	36	second	second	ADJ
geusb-8353	43	37	image	image	NOUN
geusb-8353	43	38	input	input	NOUN
geusb-8353	43	39	channel	channel	NOUN
geusb-8353	43	40	.	.	PUNCT
geusb-8353	44	1	ultimately	ultimately	ADV
geusb-8353	44	2	,	,	PUNCT
geusb-8353	44	3	we	we	PRON
geusb-8353	44	4	chose	choose	VERB
geusb-8353	44	5	not	not	PART
geusb-8353	44	6	to	to	PART
geusb-8353	44	7	do	do	VERB
geusb-8353	44	8	this	this	PRON
geusb-8353	44	9	because	because	SCONJ
geusb-8353	44	10	that	that	PRON
geusb-8353	44	11	requires	require	VERB
geusb-8353	44	12	us	we	PRON
geusb-8353	44	13	to	to	PART
geusb-8353	44	14	calculate	calculate	VERB
geusb-8353	44	15	the	the	DET
geusb-8353	44	16	fourier	fourier	ADJ
geusb-8353	44	17	transform	transform	NOUN
geusb-8353	44	18	of	of	ADP
geusb-8353	44	19	the	the	DET
geusb-8353	44	20	data	datum	NOUN
geusb-8353	44	21	input	input	NOUN
geusb-8353	44	22	every	every	DET
geusb-8353	44	23	time	time	NOUN
geusb-8353	44	24	we	we	PRON
geusb-8353	44	25	want	want	VERB
geusb-8353	44	26	to	to	PART
geusb-8353	44	27	use	use	VERB
geusb-8353	44	28	the	the	DET
geusb-8353	44	29	model	model	NOUN
geusb-8353	44	30	.	.	PUNCT
geusb-8353	45	1	this	this	PRON
geusb-8353	45	2	is	be	AUX
geusb-8353	45	3	a	a	DET
geusb-8353	45	4	disadvantage	disadvantage	NOUN
geusb-8353	45	5	because	because	SCONJ
geusb-8353	45	6	it	it	PRON
geusb-8353	45	7	slows	slow	VERB
geusb-8353	45	8	down	down	ADP
geusb-8353	45	9	the	the	DET
geusb-8353	45	10	prediction	prediction	NOUN
geusb-8353	45	11	process	process	NOUN
geusb-8353	45	12	,	,	PUNCT
geusb-8353	45	13	which	which	PRON
geusb-8353	45	14	becomes	become	VERB
geusb-8353	45	15	an	an	DET
geusb-8353	45	16	issue	issue	NOUN
geusb-8353	45	17	when	when	SCONJ
geusb-8353	45	18	applied	apply	VERB
geusb-8353	45	19	to	to	ADP
geusb-8353	45	20	very	very	ADV
geusb-8353	45	21	large	large	ADJ
geusb-8353	45	22	models	model	NOUN
geusb-8353	45	23	.	.	PUNCT
geusb-8353	46	1	the	the	DET
geusb-8353	46	2	fourier	fourier	NOUN
geusb-8353	46	3	transform	transform	NOUN
geusb-8353	46	4	could	could	AUX
geusb-8353	46	5	be	be	AUX
geusb-8353	46	6	useful	useful	ADJ
geusb-8353	46	7	if	if	SCONJ
geusb-8353	46	8	training	training	NOUN
geusb-8353	46	9	data	datum	NOUN
geusb-8353	46	10	are	be	AUX
geusb-8353	46	11	scarce	scarce	ADJ
geusb-8353	46	12	,	,	PUNCT
geusb-8353	46	13	which	which	PRON
geusb-8353	46	14	could	could	AUX
geusb-8353	46	15	be	be	AUX
geusb-8353	46	16	the	the	DET
geusb-8353	46	17	case	case	NOUN
geusb-8353	46	18	with	with	ADP
geusb-8353	46	19	non	non	ADJ
geusb-8353	46	20	-	-	ADJ
geusb-8353	46	21	synthetic	synthetic	ADJ
geusb-8353	46	22	training	training	NOUN
geusb-8353	46	23	data	datum	NOUN
geusb-8353	46	24	.	.	PUNCT
geusb-8353	47	1	at	at	ADP
geusb-8353	47	2	this	this	DET
geusb-8353	47	3	point	point	NOUN
geusb-8353	47	4	,	,	PUNCT
geusb-8353	47	5	the	the	DET
geusb-8353	47	6	network	network	NOUN
geusb-8353	47	7	is	be	AUX
geusb-8353	47	8	ready	ready	ADJ
geusb-8353	47	9	to	to	PART
geusb-8353	47	10	use	use	VERB
geusb-8353	47	11	,	,	PUNCT
geusb-8353	47	12	and	and	CCONJ
geusb-8353	47	13	only	only	ADV
geusb-8353	47	14	requires	require	VERB
geusb-8353	47	15	a	a	DET
geusb-8353	47	16	31	31	NUM
geusb-8353	47	17	×	×	NOUN
geusb-8353	47	18	31	31	NUM
geusb-8353	47	19	grid	grid	NOUN
geusb-8353	47	20	input	input	NOUN
geusb-8353	47	21	.	.	PUNCT
geusb-8353	48	1	the	the	DET
geusb-8353	48	2	training	training	NOUN
geusb-8353	48	3	can	can	AUX
geusb-8353	48	4	also	also	ADV
geusb-8353	48	5	be	be	AUX
geusb-8353	48	6	done	do	VERB
geusb-8353	48	7	with	with	ADP
geusb-8353	48	8	point	point	NOUN
geusb-8353	48	9	data	datum	NOUN
geusb-8353	48	10	.	.	PUNCT
geusb-8353	49	1	however	however	ADV
geusb-8353	49	2	,	,	PUNCT
geusb-8353	49	3	as	as	SCONJ
geusb-8353	49	4	the	the	DET
geusb-8353	49	5	cnn	cnn	PROPN
geusb-8353	49	6	is	be	AUX
geusb-8353	49	7	based	base	VERB
geusb-8353	49	8	on	on	ADP
geusb-8353	49	9	image	image	NOUN
geusb-8353	49	10	convolution	convolution	NOUN
geusb-8353	49	11	,	,	PUNCT
geusb-8353	49	12	one	one	PRON
geusb-8353	49	13	should	should	AUX
geusb-8353	49	14	choose	choose	VERB
geusb-8353	49	15	an	an	DET
geusb-8353	49	16	interpolation	interpolation	NOUN
geusb-8353	49	17	method	method	NOUN
geusb-8353	49	18	and	and	CCONJ
geusb-8353	49	19	convert	convert	VERB
geusb-8353	49	20	the	the	DET
geusb-8353	49	21	point	point	NOUN
geusb-8353	49	22	data	datum	NOUN
geusb-8353	49	23	to	to	ADP
geusb-8353	49	24	a	a	DET
geusb-8353	49	25	grid	grid	NOUN
geusb-8353	49	26	.	.	PUNCT
geusb-8353	50	1	validation	validation	NOUN
geusb-8353	50	2	we	we	PRON
geusb-8353	50	3	demonstrate	demonstrate	VERB
geusb-8353	50	4	the	the	DET
geusb-8353	50	5	advantage	advantage	NOUN
geusb-8353	50	6	of	of	ADP
geusb-8353	50	7	using	use	VERB
geusb-8353	50	8	ml	ml	NOUN
geusb-8353	50	9	for	for	ADP
geusb-8353	50	10	the	the	DET
geusb-8353	50	11	estimation	estimation	NOUN
geusb-8353	50	12	of	of	ADP
geusb-8353	50	13	the	the	DET
geusb-8353	50	14	semivariogram	semivariogram	NOUN
geusb-8353	50	15	model	model	NOUN
geusb-8353	50	16	with	with	ADP
geusb-8353	50	17	a	a	DET
geusb-8353	50	18	synthetic	synthetic	ADJ
geusb-8353	50	19	example	example	NOUN
geusb-8353	50	20	,	,	PUNCT
geusb-8353	50	21	where	where	SCONJ
geusb-8353	50	22	the	the	DET
geusb-8353	50	23	method	method	NOUN
geusb-8353	50	24	is	be	AUX
geusb-8353	50	25	compared	compare	VERB
geusb-8353	50	26	to	to	ADP
geusb-8353	50	27	the	the	DET
geusb-8353	50	28	classic	classic	ADJ
geusb-8353	50	29	method	method	NOUN
geusb-8353	50	30	of	of	ADP
geusb-8353	50	31	fitting	fit	VERB
geusb-8353	50	32	an	an	DET
geusb-8353	50	33	experimental	experimental	ADJ
geusb-8353	50	34	semivariogram	semivariogram	NOUN
geusb-8353	50	35	.	.	PUNCT
geusb-8353	51	1	we	we	PRON
geusb-8353	51	2	then	then	ADV
geusb-8353	51	3	demonstrate	demonstrate	VERB
geusb-8353	51	4	the	the	DET
geusb-8353	51	5	advantage	advantage	NOUN
geusb-8353	51	6	of	of	ADP
geusb-8353	51	7	applying	apply	VERB
geusb-8353	51	8	the	the	DET
geusb-8353	51	9	method	method	NOUN
geusb-8353	51	10	given	give	VERB
geusb-8353	51	11	an	an	DET
geusb-8353	51	12	actual	actual	ADJ
geusb-8353	51	13	use	use	NOUN
geusb-8353	51	14	case	case	NOUN
geusb-8353	51	15	.	.	PUNCT
geusb-8353	52	1	the	the	DET
geusb-8353	52	2	5	5	NUM
geusb-8353	52	3	%	%	NOUN
geusb-8353	52	4	of	of	ADP
geusb-8353	52	5	the	the	DET
geusb-8353	52	6	data	datum	NOUN
geusb-8353	52	7	,	,	PUNCT
geusb-8353	52	8	which	which	PRON
geusb-8353	52	9	we	we	PRON
geusb-8353	52	10	set	set	VERB
geusb-8353	52	11	aside	aside	ADV
geusb-8353	52	12	for	for	ADP
geusb-8353	52	13	validation	validation	NOUN
geusb-8353	52	14	,	,	PUNCT
geusb-8353	52	15	are	be	AUX
geusb-8353	52	16	fed	feed	VERB
geusb-8353	52	17	to	to	ADP
geusb-8353	52	18	the	the	DET
geusb-8353	52	19	network	network	NOUN
geusb-8353	52	20	and	and	CCONJ
geusb-8353	52	21	serve	serve	VERB
geusb-8353	52	22	as	as	ADP
geusb-8353	52	23	an	an	DET
geusb-8353	52	24	initial	initial	ADJ
geusb-8353	52	25	validation	validation	NOUN
geusb-8353	52	26	test	test	NOUN
geusb-8353	52	27	.	.	PUNCT
geusb-8353	53	1	the	the	DET
geusb-8353	53	2	size	size	NOUN
geusb-8353	53	3	of	of	ADP
geusb-8353	53	4	the	the	DET
geusb-8353	53	5	validation	validation	NOUN
geusb-8353	53	6	set	set	NOUN
geusb-8353	53	7	is	be	AUX
geusb-8353	53	8	7500	7500	NUM
geusb-8353	53	9	data	datum	NOUN
geusb-8353	53	10	points	point	NOUN
geusb-8353	53	11	.	.	PUNCT
geusb-8353	54	1	figure	figure	NOUN
geusb-8353	54	2	2	2	NUM
geusb-8353	54	3	shows	show	VERB
geusb-8353	54	4	the	the	DET
geusb-8353	54	5	distribution	distribution	NOUN
geusb-8353	54	6	of	of	ADP
geusb-8353	54	7	true	true	ADJ
geusb-8353	54	8	to	to	PART
geusb-8353	54	9	predicted	predict	VERB
geusb-8353	54	10	values	value	NOUN
geusb-8353	54	11	for	for	ADP
geusb-8353	54	12	the	the	DET
geusb-8353	54	13	range	range	NOUN
geusb-8353	54	14	and	and	CCONJ
geusb-8353	54	15	sill	sill	ADJ
geusb-8353	54	16	for	for	SCONJ
geusb-8353	54	17	the	the	DET
geusb-8353	54	18	validation	validation	NOUN
geusb-8353	54	19	data	datum	NOUN
geusb-8353	54	20	set	set	VERB
geusb-8353	54	21	.	.	PUNCT
geusb-8353	55	1	a	a	DET
geusb-8353	55	2	common	common	ADJ
geusb-8353	55	3	way	way	NOUN
geusb-8353	55	4	to	to	PART
geusb-8353	55	5	estimate	estimate	VERB
geusb-8353	55	6	the	the	DET
geusb-8353	55	7	sill	sill	NOUN
geusb-8353	55	8	is	be	AUX
geusb-8353	55	9	to	to	PART
geusb-8353	55	10	simply	simply	ADV
geusb-8353	55	11	take	take	VERB
geusb-8353	55	12	the	the	DET
geusb-8353	55	13	sample	sample	NOUN
geusb-8353	55	14	variance	variance	NOUN
geusb-8353	55	15	of	of	ADP
geusb-8353	55	16	the	the	DET
geusb-8353	55	17	scattered	scatter	VERB
geusb-8353	55	18	points	point	NOUN
geusb-8353	55	19	,	,	PUNCT
geusb-8353	55	20	which	which	PRON
geusb-8353	55	21	is	be	AUX
geusb-8353	55	22	also	also	ADV
geusb-8353	55	23	shown	show	VERB
geusb-8353	55	24	in	in	ADP
geusb-8353	55	25	fig	fig	NOUN
geusb-8353	55	26	.	.	PUNCT
geusb-8353	56	1	2	2	X
geusb-8353	56	2	.	.	X
geusb-8353	56	3	the	the	DET
geusb-8353	56	4	predicted	predict	VERB
geusb-8353	56	5	range	range	NOUN
geusb-8353	56	6	values	value	NOUN
geusb-8353	56	7	are	be	AUX
geusb-8353	56	8	very	very	ADV
geusb-8353	56	9	close	close	ADJ
geusb-8353	56	10	to	to	ADP
geusb-8353	56	11	the	the	DET
geusb-8353	56	12	true	true	ADJ
geusb-8353	56	13	values	value	NOUN
geusb-8353	56	14	for	for	ADP
geusb-8353	56	15	both	both	PRON
geusb-8353	56	16	scattered	scatter	VERB
geusb-8353	56	17	data	datum	NOUN
geusb-8353	56	18	and	and	CCONJ
geusb-8353	56	19	full	full	ADJ
geusb-8353	56	20	grids	grid	NOUN
geusb-8353	56	21	.	.	PUNCT
geusb-8353	57	1	the	the	DET
geusb-8353	57	2	predictions	prediction	NOUN
geusb-8353	57	3	using	use	VERB
geusb-8353	57	4	full	full	ADJ
geusb-8353	57	5	grids	grid	NOUN
geusb-8353	57	6	are	be	AUX
geusb-8353	57	7	a	a	DET
geusb-8353	57	8	little	little	ADV
geusb-8353	57	9	more	more	ADV
geusb-8353	57	10	accurate	accurate	ADJ
geusb-8353	57	11	,	,	PUNCT
geusb-8353	57	12	which	which	PRON
geusb-8353	57	13	we	we	PRON
geusb-8353	57	14	expected	expect	VERB
geusb-8353	57	15	since	since	SCONJ
geusb-8353	57	16	the	the	DET
geusb-8353	57	17	scattered	scatter	VERB
geusb-8353	57	18	points	point	NOUN
geusb-8353	57	19	are	be	AUX
geusb-8353	57	20	drawn	draw	VERB
geusb-8353	57	21	from	from	ADP
geusb-8353	57	22	the	the	DET
geusb-8353	57	23	full	full	ADJ
geusb-8353	57	24	grids	grid	NOUN
geusb-8353	57	25	and	and	CCONJ
geusb-8353	57	26	thus	thus	ADV
geusb-8353	57	27	contain	contain	VERB
geusb-8353	57	28	less	less	ADJ
geusb-8353	57	29	information	information	NOUN
geusb-8353	57	30	.	.	PUNCT
geusb-8353	58	1	the	the	DET
geusb-8353	58	2	predictions	prediction	NOUN
geusb-8353	58	3	for	for	ADP
geusb-8353	58	4	the	the	DET
geusb-8353	58	5	sill	sill	NOUN
geusb-8353	58	6	are	be	AUX
geusb-8353	58	7	equally	equally	ADV
geusb-8353	58	8	precise	precise	ADJ
geusb-8353	58	9	between	between	ADP
geusb-8353	58	10	the	the	DET
geusb-8353	58	11	two	two	NUM
geusb-8353	58	12	cases	case	NOUN
geusb-8353	58	13	and	and	CCONJ
geusb-8353	58	14	even	even	ADV
geusb-8353	58	15	share	share	VERB
geusb-8353	58	16	the	the	DET
geusb-8353	58	17	same	same	ADJ
geusb-8353	58	18	apparent	apparent	ADJ
geusb-8353	58	19	biases	bias	NOUN
geusb-8353	58	20	.	.	PUNCT
geusb-8353	59	1	for	for	ADP
geusb-8353	59	2	example	example	NOUN
geusb-8353	59	3	,	,	PUNCT
geusb-8353	59	4	at	at	ADP
geusb-8353	59	5	sill	sill	ADJ
geusb-8353	59	6	values	value	NOUN
geusb-8353	59	7	of	of	ADP
geusb-8353	59	8	500	500	NUM
geusb-8353	59	9	to	to	PART
geusb-8353	59	10	1500	1500	NUM
geusb-8353	59	11	,	,	PUNCT
geusb-8353	59	12	the	the	DET
geusb-8353	59	13	ml	ml	PROPN
geusb-8353	59	14	model	model	NOUN
geusb-8353	59	15	over	over	ADP
geusb-8353	59	16	-	-	PUNCT
geusb-8353	59	17	estimates	estimate	NOUN
geusb-8353	59	18	,	,	PUNCT
geusb-8353	59	19	whilst	whilst	SCONJ
geusb-8353	59	20	it	it	PRON
geusb-8353	59	21	underestimates	underestimate	VERB
geusb-8353	59	22	for	for	ADP
geusb-8353	59	23	sill	sill	ADJ
geusb-8353	59	24	values	value	NOUN
geusb-8353	59	25	above	above	ADP
geusb-8353	59	26	2000	2000	NUM
geusb-8353	59	27	.	.	PUNCT
geusb-8353	60	1	although	although	SCONJ
geusb-8353	60	2	we	we	PRON
geusb-8353	60	3	did	do	AUX
geusb-8353	60	4	not	not	PART
geusb-8353	60	5	use	use	VERB
geusb-8353	60	6	bias	bias	NOUN
geusb-8353	60	7	description	description	NOUN
geusb-8353	60	8	,	,	PUNCT
geusb-8353	60	9	it	it	PRON
geusb-8353	60	10	is	be	AUX
geusb-8353	60	11	possible	possible	ADJ
geusb-8353	60	12	by	by	ADP
geusb-8353	60	13	fitting	fit	VERB
geusb-8353	60	14	a	a	DET
geusb-8353	60	15	suitable	suitable	ADJ
geusb-8353	60	16	polynomial	polynomial	ADJ
geusb-8353	60	17	function	function	NOUN
geusb-8353	60	18	between	between	ADP
geusb-8353	60	19	prediction	prediction	NOUN
geusb-8353	60	20	and	and	CCONJ
geusb-8353	60	21	actual	actual	ADJ
geusb-8353	60	22	values	value	NOUN
geusb-8353	60	23	.	.	PUNCT
geusb-8353	61	1	correcting	correct	VERB
geusb-8353	61	2	the	the	DET
geusb-8353	61	3	estimates	estimate	NOUN
geusb-8353	61	4	to	to	PART
geusb-8353	61	5	account	account	VERB
geusb-8353	61	6	for	for	ADP
geusb-8353	61	7	bias	bias	NOUN
geusb-8353	61	8	is	be	AUX
geusb-8353	61	9	then	then	ADV
geusb-8353	61	10	straightforward	straightforward	ADJ
geusb-8353	61	11	.	.	PUNCT
geusb-8353	62	1	alternatively	alternatively	ADV
geusb-8353	62	2	,	,	PUNCT
geusb-8353	62	3	it	it	PRON
geusb-8353	62	4	is	be	AUX
geusb-8353	62	5	reasonable	reasonable	ADJ
geusb-8353	62	6	to	to	PART
geusb-8353	62	7	assume	assume	VERB
geusb-8353	62	8	that	that	SCONJ
geusb-8353	62	9	improvements	improvement	NOUN
geusb-8353	62	10	in	in	ADP
geusb-8353	62	11	the	the	DET
geusb-8353	62	12	nn	nn	PROPN
geusb-8353	62	13	itself	itself	PRON
geusb-8353	62	14	could	could	AUX
geusb-8353	62	15	eliminate	eliminate	VERB
geusb-8353	62	16	the	the	DET
geusb-8353	62	17	bias	bias	NOUN
geusb-8353	62	18	.	.	PUNCT
geusb-8353	63	1	the	the	DET
geusb-8353	63	2	variance	variance	NOUN
geusb-8353	63	3	estimate	estimate	NOUN
geusb-8353	63	4	is	be	AUX
geusb-8353	63	5	less	less	ADV
geusb-8353	63	6	precise	precise	ADJ
geusb-8353	63	7	but	but	CCONJ
geusb-8353	63	8	has	have	VERB
geusb-8353	63	9	no	no	DET
geusb-8353	63	10	bias	bias	NOUN
geusb-8353	63	11	.	.	PUNCT
geusb-8353	64	1	validation	validation	NOUN
geusb-8353	64	2	on	on	ADP
geusb-8353	64	3	synthetic	synthetic	ADJ
geusb-8353	64	4	data	datum	NOUN
geusb-8353	64	5	we	we	PRON
geusb-8353	64	6	take	take	VERB
geusb-8353	64	7	a	a	DET
geusb-8353	64	8	set	set	NOUN
geusb-8353	64	9	of	of	ADP
geusb-8353	64	10	1000	1000	NUM
geusb-8353	64	11	new	new	ADJ
geusb-8353	64	12	synthetic	synthetic	ADJ
geusb-8353	64	13	realisations	realisation	NOUN
geusb-8353	64	14	of	of	ADP
geusb-8353	64	15	size	size	NOUN
geusb-8353	64	16	31	31	NUM
geusb-8353	64	17	×	×	NOUN
geusb-8353	64	18	31	31	NUM
geusb-8353	64	19	,	,	PUNCT
geusb-8353	64	20	each	each	PRON
geusb-8353	64	21	with	with	ADP
geusb-8353	64	22	120	120	NUM
geusb-8353	64	23	randomly	randomly	ADV
geusb-8353	64	24	drawn	draw	VERB
geusb-8353	64	25	points	point	NOUN
geusb-8353	64	26	,	,	PUNCT
geusb-8353	64	27	and	and	CCONJ
geusb-8353	64	28	we	we	PRON
geusb-8353	64	29	use	use	VERB
geusb-8353	64	30	these	these	DET
geusb-8353	64	31	points	point	NOUN
geusb-8353	64	32	to	to	ADP
geusb-8353	64	33	:	:	PUNCT
geusb-8353	64	34	1	1	X
geusb-8353	64	35	.	.	PUNCT
geusb-8353	64	36	  	  	SPACE
geusb-8353	64	37	perform	perform	VERB
geusb-8353	64	38	a	a	DET
geusb-8353	64	39	traditional	traditional	ADJ
geusb-8353	64	40	semivariogram	semivariogram	NOUN
geusb-8353	64	41	analysis	analysis	NOUN
geusb-8353	64	42	with	with	ADP
geusb-8353	64	43	the	the	DET
geusb-8353	64	44	following	follow	VERB
geusb-8353	64	45	steps	step	NOUN
geusb-8353	64	46	:	:	PUNCT
geusb-8353	64	47	a.	a.	NOUN
geusb-8353	64	48	 	 	SPACE
geusb-8353	64	49	calculate	calculate	VERB
geusb-8353	64	50	an	an	DET
geusb-8353	64	51	empirical	empirical	ADJ
geusb-8353	64	52	semivariogram	semivariogram	NOUN
geusb-8353	64	53	from	from	ADP
geusb-8353	64	54	the	the	DET
geusb-8353	64	55	points	point	NOUN
geusb-8353	64	56	.	.	PUNCT
geusb-8353	65	1	b.	b.	PROPN
geusb-8353	65	2	 	 	SPACE
geusb-8353	65	3	fit	fit	VERB
geusb-8353	65	4	a	a	DET
geusb-8353	65	5	gaussian	gaussian	ADJ
geusb-8353	65	6	semivariance	semivariance	NOUN
geusb-8353	65	7	model	model	NOUN
geusb-8353	65	8	to	to	ADP
geusb-8353	65	9	the	the	DET
geusb-8353	65	10	semivariogram	semivariogram	NOUN
geusb-8353	65	11	using	use	VERB
geusb-8353	65	12	a	a	DET
geusb-8353	65	13	weighted	weight	VERB
geusb-8353	65	14	leastsquares	leastsquare	NOUN
geusb-8353	65	15	approach	approach	NOUN
geusb-8353	65	16	,	,	PUNCT
geusb-8353	65	17	where	where	SCONJ
geusb-8353	65	18	points	point	VERB
geusb-8353	65	19	closer	close	ADV
geusb-8353	65	20	to	to	ADP
geusb-8353	65	21	the	the	DET
geusb-8353	65	22	origin	origin	NOUN
geusb-8353	65	23	have	have	VERB
geusb-8353	65	24	greater	great	ADJ
geusb-8353	65	25	weight	weight	NOUN
geusb-8353	65	26	.	.	PUNCT
geusb-8353	66	1	2	2	X
geusb-8353	66	2	.	.	PUNCT
geusb-8353	66	3	  	  	SPACE
geusb-8353	66	4	predict	predict	VERB
geusb-8353	66	5	the	the	DET
geusb-8353	66	6	sill	sill	ADJ
geusb-8353	66	7	and	and	CCONJ
geusb-8353	66	8	range	range	VERB
geusb-8353	66	9	directly	directly	ADV
geusb-8353	66	10	with	with	ADP
geusb-8353	66	11	our	our	PRON
geusb-8353	66	12	cnn	cnn	NOUN
geusb-8353	66	13	with	with	ADP
geusb-8353	66	14	the	the	DET
geusb-8353	66	15	following	follow	VERB
geusb-8353	66	16	steps	step	NOUN
geusb-8353	66	17	:	:	PUNCT
geusb-8353	66	18	a.	a.	NOUN
geusb-8353	66	19	  	  	SPACE
geusb-8353	66	20	interpolate	interpolate	VERB
geusb-8353	66	21	the	the	DET
geusb-8353	66	22	120	120	NUM
geusb-8353	66	23	points	point	NOUN
geusb-8353	66	24	onto	onto	ADP
geusb-8353	66	25	the	the	DET
geusb-8353	66	26	entire	entire	ADJ
geusb-8353	66	27	grid	grid	NOUN
geusb-8353	66	28	.	.	PUNCT
geusb-8353	67	1	b.	b.	PROPN
geusb-8353	67	2	 	 	SPACE
geusb-8353	67	3	pass	pass	VERB
geusb-8353	67	4	the	the	DET
geusb-8353	67	5	interpolated	interpolate	VERB
geusb-8353	67	6	grid	grid	NOUN
geusb-8353	67	7	through	through	ADP
geusb-8353	67	8	the	the	DET
geusb-8353	67	9	cnn	cnn	PROPN
geusb-8353	67	10	.	.	PUNCT
geusb-8353	68	1	we	we	PRON
geusb-8353	68	2	then	then	ADV
geusb-8353	68	3	compare	compare	VERB
geusb-8353	68	4	the	the	DET
geusb-8353	68	5	accuracy	accuracy	NOUN
geusb-8353	68	6	of	of	ADP
geusb-8353	68	7	the	the	DET
geusb-8353	68	8	estimates	estimate	NOUN
geusb-8353	68	9	with	with	ADP
geusb-8353	68	10	these	these	DET
geusb-8353	68	11	two	two	NUM
geusb-8353	68	12	methods	method	NOUN
geusb-8353	68	13	as	as	ADV
geusb-8353	68	14	well	well	ADV
geusb-8353	68	15	as	as	ADP
geusb-8353	68	16	the	the	DET
geusb-8353	68	17	time	time	NOUN
geusb-8353	68	18	it	it	PRON
geusb-8353	68	19	takes	take	VERB
geusb-8353	68	20	to	to	PART
geusb-8353	68	21	complete	complete	VERB
geusb-8353	68	22	.	.	PUNCT
geusb-8353	69	1	the	the	DET
geusb-8353	69	2	result	result	NOUN
geusb-8353	69	3	of	of	ADP
geusb-8353	69	4	the	the	DET
geusb-8353	69	5	accuracy	accuracy	NOUN
geusb-8353	69	6	comparison	comparison	NOUN
geusb-8353	69	7	is	be	AUX
geusb-8353	69	8	shown	show	VERB
geusb-8353	69	9	in	in	ADP
geusb-8353	69	10	fig	fig	NOUN
geusb-8353	69	11	.	.	PUNCT
geusb-8353	70	1	3	3	NUM
geusb-8353	70	2	for	for	ADP
geusb-8353	70	3	eight	eight	NUM
geusb-8353	70	4	different	different	ADJ
geusb-8353	70	5	realisations	realisation	NOUN
geusb-8353	70	6	.	.	PUNCT
geusb-8353	71	1	depending	depend	VERB
geusb-8353	71	2	on	on	ADP
geusb-8353	71	3	the	the	DET
geusb-8353	71	4	specific	specific	ADJ
geusb-8353	71	5	realisation	realisation	NOUN
geusb-8353	71	6	,	,	PUNCT
geusb-8353	71	7	it	it	PRON
geusb-8353	71	8	varies	vary	VERB
geusb-8353	71	9	whether	whether	SCONJ
geusb-8353	71	10	the	the	DET
geusb-8353	71	11	fitted	fit	VERB
geusb-8353	71	12	model	model	NOUN
geusb-8353	71	13	(	(	PUNCT
geusb-8353	71	14	black	black	ADJ
geusb-8353	71	15	line	line	NOUN
geusb-8353	71	16	)	)	PUNCT
geusb-8353	71	17	or	or	CCONJ
geusb-8353	71	18	cnn	cnn	PROPN
geusb-8353	71	19	(	(	PUNCT
geusb-8353	71	20	blue	blue	ADJ
geusb-8353	71	21	line	line	NOUN
geusb-8353	71	22	)	)	PUNCT
geusb-8353	71	23	is	be	AUX
geusb-8353	71	24	better	well	ADJ
geusb-8353	71	25	at	at	ADP
geusb-8353	71	26	resolving	resolve	VERB
geusb-8353	71	27	the	the	DET
geusb-8353	71	28	true	true	ADJ
geusb-8353	71	29	model	model	NOUN
geusb-8353	71	30	(	(	PUNCT
geusb-8353	71	31	red	red	ADJ
geusb-8353	71	32	line	line	NOUN
geusb-8353	71	33	)	)	PUNCT
geusb-8353	71	34	.	.	PUNCT
geusb-8353	72	1	across	across	ADP
geusb-8353	72	2	all	all	DET
geusb-8353	72	3	1000	1000	NUM
geusb-8353	72	4	realisations	realisation	NOUN
geusb-8353	72	5	,	,	PUNCT
geusb-8353	72	6	we	we	PRON
geusb-8353	72	7	see	see	VERB
geusb-8353	72	8	that	that	SCONJ
geusb-8353	72	9	the	the	DET
geusb-8353	72	10	cnn	cnn	PROPN
geusb-8353	72	11	is	be	AUX
geusb-8353	72	12	slightly	slightly	ADV
geusb-8353	72	13	better	well	ADJ
geusb-8353	72	14	at	at	ADP
geusb-8353	72	15	approximating	approximate	VERB
geusb-8353	72	16	large	large	ADJ
geusb-8353	72	17	values	value	NOUN
geusb-8353	72	18	for	for	ADP
geusb-8353	72	19	the	the	DET
geusb-8353	72	20	range	range	NOUN
geusb-8353	72	21	than	than	ADP
geusb-8353	72	22	the	the	DET
geusb-8353	72	23	traditional	traditional	ADJ
geusb-8353	72	24	approach	approach	NOUN
geusb-8353	72	25	,	,	PUNCT
geusb-8353	72	26	whereas	whereas	SCONJ
geusb-8353	72	27	the	the	DET
geusb-8353	72	28	traditional	traditional	ADJ
geusb-8353	72	29	method	method	NOUN
geusb-8353	72	30	is	be	AUX
geusb-8353	72	31	slightly	slightly	ADV
geusb-8353	72	32	better	well	ADJ
geusb-8353	72	33	at	at	ADP
geusb-8353	72	34	low	low	ADJ
geusb-8353	72	35	values	value	NOUN
geusb-8353	72	36	.	.	PUNCT
geusb-8353	73	1	in	in	ADP
geusb-8353	73	2	general	general	ADJ
geusb-8353	73	3	,	,	PUNCT
geusb-8353	73	4	both	both	DET
geusb-8353	73	5	methods	method	NOUN
geusb-8353	73	6	have	have	VERB
geusb-8353	73	7	similar	similar	ADJ
geusb-8353	73	8	performance	performance	NOUN
geusb-8353	73	9	.	.	PUNCT
geusb-8353	74	1	however	however	ADV
geusb-8353	74	2	,	,	PUNCT
geusb-8353	74	3	the	the	DET
geusb-8353	74	4	time	time	NOUN
geusb-8353	74	5	that	that	PRON
geusb-8353	74	6	the	the	DET
geusb-8353	74	7	methods	method	NOUN
geusb-8353	74	8	need	need	VERB
geusb-8353	74	9	to	to	PART
geusb-8353	74	10	reach	reach	VERB
geusb-8353	74	11	the	the	DET
geusb-8353	74	12	predictions	prediction	NOUN
geusb-8353	74	13	is	be	AUX
geusb-8353	74	14	not	not	PART
geusb-8353	74	15	the	the	DET
geusb-8353	74	16	same	same	ADJ
geusb-8353	74	17	.	.	PUNCT
geusb-8353	75	1	during	during	ADP
geusb-8353	75	2	the	the	DET
geusb-8353	75	3	test	test	NOUN
geusb-8353	75	4	,	,	PUNCT
geusb-8353	75	5	we	we	PRON
geusb-8353	75	6	fig	fig	VERB
geusb-8353	75	7	.	.	PUNCT
geusb-8353	76	1	1	1	NUM
geusb-8353	76	2	 	 	SPACE
geusb-8353	76	3	synthetic	synthetic	ADJ
geusb-8353	76	4	training	training	NOUN
geusb-8353	76	5	data	datum	NOUN
geusb-8353	76	6	produced	produce	VERB
geusb-8353	76	7	using	use	VERB
geusb-8353	76	8	gaussian	gaussian	ADJ
geusb-8353	76	9	semivariance	semivariance	NOUN
geusb-8353	76	10	models	model	NOUN
geusb-8353	76	11	with	with	ADP
geusb-8353	76	12	random	random	ADJ
geusb-8353	76	13	sill	sill	ADJ
geusb-8353	76	14	and	and	CCONJ
geusb-8353	76	15	range	range	VERB
geusb-8353	76	16	,	,	PUNCT
geusb-8353	76	17	with	with	ADP
geusb-8353	76	18	a	a	DET
geusb-8353	76	19	component	component	NOUN
geusb-8353	76	20	of	of	ADP
geusb-8353	76	21	noise	noise	NOUN
geusb-8353	76	22	.	.	PUNCT
geusb-8353	77	1	the	the	DET
geusb-8353	77	2	circles	circle	NOUN
geusb-8353	77	3	show	show	VERB
geusb-8353	77	4	120	120	NUM
geusb-8353	77	5	randomly	randomly	ADV
geusb-8353	77	6	drawn	draw	VERB
geusb-8353	77	7	points	point	NOUN
geusb-8353	77	8	from	from	ADP
geusb-8353	77	9	each	each	DET
geusb-8353	77	10	realisation	realisation	NOUN
geusb-8353	77	11	.	.	PUNCT
geusb-8353	78	1	shading	shading	NOUN
geusb-8353	78	2	:	:	PUNCT
geusb-8353	78	3	smallest	small	ADJ
geusb-8353	78	4	values	value	NOUN
geusb-8353	78	5	are	be	AUX
geusb-8353	78	6	shown	show	VERB
geusb-8353	78	7	in	in	ADP
geusb-8353	78	8	blue	blue	ADJ
geusb-8353	78	9	and	and	CCONJ
geusb-8353	78	10	largest	large	ADJ
geusb-8353	78	11	values	value	NOUN
geusb-8353	78	12	are	be	AUX
geusb-8353	78	13	shown	show	VERB
geusb-8353	78	14	in	in	ADP
geusb-8353	78	15	yellow	yellow	ADJ
geusb-8353	78	16	,	,	PUNCT
geusb-8353	78	17	with	with	ADP
geusb-8353	78	18	in	in	ADP
geusb-8353	78	19	-	-	PUNCT
geusb-8353	78	20	between	between	ADP
geusb-8353	78	21	values	value	NOUN
geusb-8353	78	22	shown	show	VERB
geusb-8353	78	23	in	in	ADP
geusb-8353	78	24	green	green	ADJ
geusb-8353	78	25	.	.	PUNCT
geusb-8353	79	1	sill	sill	ADV
geusb-8353	79	2	:	:	PUNCT
geusb-8353	79	3	455.4	455.4	NUM
geusb-8353	79	4	range	range	NOUN
geusb-8353	79	5	:	:	PUNCT
geusb-8353	79	6	2713	2713	NUM
geusb-8353	79	7	sill	sill	NOUN
geusb-8353	79	8	:	:	PUNCT
geusb-8353	79	9	499.1	499.1	NUM
geusb-8353	79	10	range	range	NOUN
geusb-8353	79	11	:	:	PUNCT
geusb-8353	79	12	1577	1577	NUM
geusb-8353	79	13	sill	sill	ADJ
geusb-8353	79	14	:	:	PUNCT
geusb-8353	79	15	1054	1054	NUM
geusb-8353	79	16	range	range	NOUN
geusb-8353	79	17	:	:	PUNCT
geusb-8353	79	18	1900	1900	NUM
geusb-8353	79	19	sill	sill	NOUN
geusb-8353	79	20	:	:	PUNCT
geusb-8353	79	21	431	431	NUM
geusb-8353	79	22	range	range	NOUN
geusb-8353	79	23	:	:	PUNCT
geusb-8353	79	24	2327	2327	NUM
geusb-8353	79	25	sill	sill	NOUN
geusb-8353	79	26	:	:	PUNCT
geusb-8353	79	27	389.5	389.5	NUM
geusb-8353	79	28	range	range	NOUN
geusb-8353	79	29	:	:	PUNCT
geusb-8353	79	30	1374	1374	NUM
geusb-8353	79	31	sill	sill	ADJ
geusb-8353	79	32	:	:	PUNCT
geusb-8353	79	33	1080	1080	NUM
geusb-8353	79	34	range	range	NOUN
geusb-8353	79	35	:	:	PUNCT
geusb-8353	79	36	411.2	411.2	NUM
geusb-8353	79	37	sill	sill	NOUN
geusb-8353	79	38	:	:	PUNCT
geusb-8353	79	39	187	187	NUM
geusb-8353	79	40	range	range	NOUN
geusb-8353	79	41	:	:	PUNCT
geusb-8353	79	42	1002	1002	NUM
geusb-8353	79	43	sill	sill	NOUN
geusb-8353	79	44	:	:	PUNCT
geusb-8353	79	45	575.7	575.7	NUM
geusb-8353	79	46	range	range	NOUN
geusb-8353	79	47	:	:	PUNCT
geusb-8353	79	48	2446	2446	NUM
geusb-8353	79	49	sill	sill	NOUN
geusb-8353	79	50	:	:	PUNCT
geusb-8353	79	51	206.6	206.6	NUM
geusb-8353	79	52	range	range	NOUN
geusb-8353	79	53	:	:	PUNCT
geusb-8353	79	54	455.1	455.1	NUM
geusb-8353	79	55	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	79	56	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	79	57	falk	falk	NOUN
geusb-8353	79	58	&	&	CCONJ
geusb-8353	79	59	madsen	madsen	PROPN
geusb-8353	79	60	2023	2023	NUM
geusb-8353	79	61	:	:	PUNCT
geusb-8353	79	62	geus	geus	NOUN
geusb-8353	79	63	bulletin	bulletin	NOUN
geusb-8353	79	64	53	53	NUM
geusb-8353	79	65	.	.	PUNCT
geusb-8353	79	66	8353	8353	NUM
geusb-8353	79	67	.	.	PUNCT
geusb-8353	80	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	80	2	4	4	NUM
geusb-8353	80	3	of	of	ADP
geusb-8353	80	4	7	7	NUM
geusb-8353	80	5	www.geusbul	www.geusbul	NOUN
geusb-8353	80	6	let	let	VERB
geusb-8353	80	7	in.org	in.org	PROPN
geusb-8353	80	8	recorded	record	VERB
geusb-8353	80	9	the	the	DET
geusb-8353	80	10	time	time	NOUN
geusb-8353	80	11	spent	spend	VERB
geusb-8353	80	12	for	for	ADP
geusb-8353	80	13	each	each	DET
geusb-8353	80	14	approach	approach	NOUN
geusb-8353	80	15	and	and	CCONJ
geusb-8353	80	16	calculated	calculate	VERB
geusb-8353	80	17	the	the	DET
geusb-8353	80	18	average	average	ADJ
geusb-8353	80	19	time	time	NOUN
geusb-8353	80	20	spent	spend	VERB
geusb-8353	80	21	per	per	ADP
geusb-8353	80	22	model	model	NOUN
geusb-8353	80	23	.	.	PUNCT
geusb-8353	81	1	the	the	DET
geusb-8353	81	2	cnn	cnn	PROPN
geusb-8353	81	3	approach	approach	NOUN
geusb-8353	81	4	based	base	VERB
geusb-8353	81	5	on	on	ADP
geusb-8353	81	6	existing	exist	VERB
geusb-8353	81	7	31	31	NUM
geusb-8353	81	8	×	×	NOUN
geusb-8353	81	9	31	31	NUM
geusb-8353	81	10	grids	grid	NOUN
geusb-8353	81	11	was	be	AUX
geusb-8353	81	12	able	able	ADJ
geusb-8353	81	13	to	to	PART
geusb-8353	81	14	estimate	estimate	VERB
geusb-8353	81	15	130	130	NUM
geusb-8353	81	16	semivariogram	semivariogram	NOUN
geusb-8353	81	17	models	model	NOUN
geusb-8353	81	18	per	per	ADP
geusb-8353	81	19	second	second	ADJ
geusb-8353	81	20	,	,	PUNCT
geusb-8353	81	21	whilst	whilst	SCONJ
geusb-8353	81	22	for	for	ADP
geusb-8353	81	23	scattered	scattered	ADJ
geusb-8353	81	24	data	datum	NOUN
geusb-8353	81	25	,	,	PUNCT
geusb-8353	81	26	111	111	NUM
geusb-8353	81	27	models	model	NOUN
geusb-8353	81	28	could	could	AUX
geusb-8353	81	29	be	be	AUX
geusb-8353	81	30	estimated	estimate	VERB
geusb-8353	81	31	per	per	ADP
geusb-8353	81	32	second	second	ADJ
geusb-8353	81	33	.	.	PUNCT
geusb-8353	82	1	meanwhile	meanwhile	ADV
geusb-8353	82	2	,	,	PUNCT
geusb-8353	82	3	the	the	DET
geusb-8353	82	4	traditional	traditional	ADJ
geusb-8353	82	5	semivariogram	semivariogram	NOUN
geusb-8353	82	6	analysis	analysis	NOUN
geusb-8353	82	7	,	,	PUNCT
geusb-8353	82	8	where	where	SCONJ
geusb-8353	82	9	a	a	DET
geusb-8353	82	10	model	model	NOUN
geusb-8353	82	11	is	be	AUX
geusb-8353	82	12	fitted	fit	VERB
geusb-8353	82	13	to	to	ADP
geusb-8353	82	14	an	an	DET
geusb-8353	82	15	experimental	experimental	ADJ
geusb-8353	82	16	semivariogram	semivariogram	NOUN
geusb-8353	82	17	,	,	PUNCT
geusb-8353	82	18	could	could	AUX
geusb-8353	82	19	only	only	ADV
geusb-8353	82	20	estimate	estimate	VERB
geusb-8353	82	21	1.72	1.72	NUM
geusb-8353	82	22	models	model	NOUN
geusb-8353	82	23	per	per	ADP
geusb-8353	82	24	second	second	NOUN
geusb-8353	82	25	.	.	PUNCT
geusb-8353	83	1	of	of	ADP
geusb-8353	83	2	course	course	ADV
geusb-8353	83	3	,	,	PUNCT
geusb-8353	83	4	these	these	DET
geusb-8353	83	5	numbers	number	NOUN
geusb-8353	83	6	depend	depend	VERB
geusb-8353	83	7	on	on	ADP
geusb-8353	83	8	the	the	DET
geusb-8353	83	9	computational	computational	ADJ
geusb-8353	83	10	resources	resource	NOUN
geusb-8353	83	11	available	available	ADJ
geusb-8353	83	12	,	,	PUNCT
geusb-8353	83	13	but	but	CCONJ
geusb-8353	83	14	the	the	DET
geusb-8353	83	15	important	important	ADJ
geusb-8353	83	16	point	point	NOUN
geusb-8353	83	17	is	be	AUX
geusb-8353	83	18	the	the	DET
geusb-8353	83	19	relative	relative	ADJ
geusb-8353	83	20	difference	difference	NOUN
geusb-8353	83	21	in	in	ADP
geusb-8353	83	22	computation	computation	NOUN
geusb-8353	83	23	time	time	NOUN
geusb-8353	83	24	between	between	ADP
geusb-8353	83	25	the	the	DET
geusb-8353	83	26	methods	method	NOUN
geusb-8353	83	27	.	.	PUNCT
geusb-8353	84	1	the	the	DET
geusb-8353	84	2	time	time	NOUN
geusb-8353	84	3	consumption	consumption	NOUN
geusb-8353	84	4	of	of	ADP
geusb-8353	84	5	the	the	DET
geusb-8353	84	6	ml	ml	PROPN
geusb-8353	84	7	approach	approach	NOUN
geusb-8353	84	8	is	be	AUX
geusb-8353	84	9	only	only	ADV
geusb-8353	84	10	slightly	slightly	ADV
geusb-8353	84	11	larger	large	ADJ
geusb-8353	84	12	when	when	SCONJ
geusb-8353	84	13	interpolating	interpolate	VERB
geusb-8353	84	14	point	point	NOUN
geusb-8353	84	15	data	datum	NOUN
geusb-8353	84	16	,	,	PUNCT
geusb-8353	84	17	suggesting	suggest	VERB
geusb-8353	84	18	that	that	SCONJ
geusb-8353	84	19	the	the	DET
geusb-8353	84	20	cnn	cnn	PROPN
geusb-8353	84	21	consumes	consume	VERB
geusb-8353	84	22	most	most	ADJ
geusb-8353	84	23	of	of	ADP
geusb-8353	84	24	the	the	DET
geusb-8353	84	25	time	time	NOUN
geusb-8353	84	26	and	and	CCONJ
geusb-8353	84	27	not	not	PART
geusb-8353	84	28	the	the	DET
geusb-8353	84	29	interpolation	interpolation	NOUN
geusb-8353	84	30	itself	itself	PRON
geusb-8353	84	31	.	.	PUNCT
geusb-8353	85	1	meanwhile	meanwhile	ADV
geusb-8353	85	2	,	,	PUNCT
geusb-8353	85	3	the	the	DET
geusb-8353	85	4	traditional	traditional	ADJ
geusb-8353	85	5	approach	approach	NOUN
geusb-8353	85	6	of	of	ADP
geusb-8353	85	7	fitting	fitting	ADJ
geusb-8353	85	8	takes	take	VERB
geusb-8353	85	9	about	about	ADV
geusb-8353	85	10	60	60	NUM
geusb-8353	85	11	times	time	NOUN
geusb-8353	85	12	more	more	ADJ
geusb-8353	85	13	computation	computation	NOUN
geusb-8353	85	14	time	time	NOUN
geusb-8353	85	15	.	.	PUNCT
geusb-8353	86	1	the	the	DET
geusb-8353	86	2	time	time	NOUN
geusb-8353	86	3	consumption	consumption	NOUN
geusb-8353	86	4	varies	vary	VERB
geusb-8353	86	5	quite	quite	DET
geusb-8353	86	6	a	a	DET
geusb-8353	86	7	bit	bit	NOUN
geusb-8353	86	8	for	for	ADP
geusb-8353	86	9	the	the	DET
geusb-8353	86	10	traditional	traditional	ADJ
geusb-8353	86	11	semivariogram	semivariogram	NOUN
geusb-8353	86	12	analysis	analysis	NOUN
geusb-8353	86	13	approach	approach	NOUN
geusb-8353	86	14	,	,	PUNCT
geusb-8353	86	15	depending	depend	VERB
geusb-8353	86	16	on	on	ADP
geusb-8353	86	17	the	the	DET
geusb-8353	86	18	number	number	NOUN
geusb-8353	86	19	of	of	ADP
geusb-8353	86	20	data	datum	NOUN
geusb-8353	86	21	points	point	NOUN
geusb-8353	86	22	used	use	VERB
geusb-8353	86	23	.	.	PUNCT
geusb-8353	87	1	by	by	ADP
geusb-8353	87	2	using	use	VERB
geusb-8353	87	3	only	only	ADV
geusb-8353	87	4	60	60	NUM
geusb-8353	87	5	points	point	NOUN
geusb-8353	87	6	,	,	PUNCT
geusb-8353	87	7	it	it	PRON
geusb-8353	87	8	may	may	AUX
geusb-8353	87	9	complete	complete	VERB
geusb-8353	87	10	as	as	ADV
geusb-8353	87	11	many	many	ADJ
geusb-8353	87	12	as	as	ADP
geusb-8353	87	13	30	30	NUM
geusb-8353	87	14	models	model	NOUN
geusb-8353	87	15	per	per	ADP
geusb-8353	87	16	second	second	NOUN
geusb-8353	87	17	;	;	PUNCT
geusb-8353	87	18	however	however	ADV
geusb-8353	87	19	,	,	PUNCT
geusb-8353	87	20	this	this	PRON
geusb-8353	87	21	is	be	AUX
geusb-8353	87	22	still	still	ADV
geusb-8353	87	23	about	about	ADV
geusb-8353	87	24	one	one	NUM
geusb-8353	87	25	-	-	PUNCT
geusb-8353	87	26	fourth	fourth	NOUN
geusb-8353	87	27	of	of	ADP
geusb-8353	87	28	the	the	DET
geusb-8353	87	29	speed	speed	NOUN
geusb-8353	87	30	of	of	ADP
geusb-8353	87	31	the	the	DET
geusb-8353	87	32	cnn	cnn	PROPN
geusb-8353	87	33	and	and	CCONJ
geusb-8353	87	34	with	with	ADP
geusb-8353	87	35	a	a	DET
geusb-8353	87	36	significant	significant	ADJ
geusb-8353	87	37	loss	loss	NOUN
geusb-8353	87	38	of	of	ADP
geusb-8353	87	39	accuracy	accuracy	NOUN
geusb-8353	87	40	.	.	PUNCT
geusb-8353	88	1	furthermore	furthermore	ADV
geusb-8353	88	2	,	,	PUNCT
geusb-8353	88	3	the	the	DET
geusb-8353	88	4	cnn	cnn	PROPN
geusb-8353	88	5	may	may	AUX
geusb-8353	88	6	also	also	ADV
geusb-8353	88	7	be	be	AUX
geusb-8353	88	8	optimised	optimise	VERB
geusb-8353	88	9	to	to	PART
geusb-8353	88	10	become	become	VERB
geusb-8353	88	11	more	more	ADV
geusb-8353	88	12	efficient	efficient	ADJ
geusb-8353	88	13	.	.	PUNCT
geusb-8353	89	1	validation	validation	NOUN
geusb-8353	89	2	by	by	ADP
geusb-8353	89	3	application	application	NOUN
geusb-8353	89	4	of	of	ADP
geusb-8353	89	5	the	the	DET
geusb-8353	89	6	method	method	NOUN
geusb-8353	89	7	besides	besides	SCONJ
geusb-8353	89	8	the	the	DET
geusb-8353	89	9	validation	validation	NOUN
geusb-8353	89	10	on	on	ADP
geusb-8353	89	11	synthetic	synthetic	ADJ
geusb-8353	89	12	data	datum	NOUN
geusb-8353	89	13	,	,	PUNCT
geusb-8353	89	14	we	we	PRON
geusb-8353	89	15	also	also	ADV
geusb-8353	89	16	validate	validate	VERB
geusb-8353	89	17	our	our	PRON
geusb-8353	89	18	method	method	NOUN
geusb-8353	89	19	by	by	ADP
geusb-8353	89	20	demonstrating	demonstrate	VERB
geusb-8353	89	21	how	how	SCONJ
geusb-8353	89	22	it	it	PRON
geusb-8353	89	23	can	can	AUX
geusb-8353	89	24	be	be	AUX
geusb-8353	89	25	used	use	VERB
geusb-8353	89	26	to	to	PART
geusb-8353	89	27	solve	solve	VERB
geusb-8353	89	28	a	a	DET
geusb-8353	89	29	real	real	ADJ
geusb-8353	89	30	problem	problem	NOUN
geusb-8353	89	31	of	of	ADP
geusb-8353	89	32	obtaining	obtain	VERB
geusb-8353	89	33	non	non	ADJ
geusb-8353	89	34	-	-	ADJ
geusb-8353	89	35	stationary	stationary	ADJ
geusb-8353	89	36	statistical	statistical	ADJ
geusb-8353	89	37	properties	property	NOUN
geusb-8353	89	38	.	.	PUNCT
geusb-8353	90	1	when	when	SCONJ
geusb-8353	90	2	dealing	deal	VERB
geusb-8353	90	3	with	with	ADP
geusb-8353	90	4	models	model	NOUN
geusb-8353	90	5	with	with	ADP
geusb-8353	90	6	non	non	ADJ
geusb-8353	90	7	-	-	ADJ
geusb-8353	90	8	stationary	stationary	ADJ
geusb-8353	90	9	statistics	statistic	NOUN
geusb-8353	90	10	,	,	PUNCT
geusb-8353	90	11	such	such	ADJ
geusb-8353	90	12	as	as	ADP
geusb-8353	90	13	very	very	ADV
geusb-8353	90	14	large	large	ADJ
geusb-8353	90	15	models	model	NOUN
geusb-8353	90	16	like	like	ADP
geusb-8353	90	17	the	the	DET
geusb-8353	90	18	dk	dk	NOUN
geusb-8353	90	19	-	-	NOUN
geusb-8353	90	20	model	model	NOUN
geusb-8353	90	21	(	(	PUNCT
geusb-8353	90	22	stisen	stisen	PROPN
geusb-8353	90	23	et	et	PROPN
geusb-8353	90	24	al	al	PROPN
geusb-8353	90	25	.	.	PROPN
geusb-8353	90	26	2020	2020	NUM
geusb-8353	90	27	)	)	PUNCT
geusb-8353	90	28	,	,	PUNCT
geusb-8353	90	29	using	use	VERB
geusb-8353	90	30	a	a	DET
geusb-8353	90	31	single	single	ADJ
geusb-8353	90	32	semivariogram	semivariogram	NOUN
geusb-8353	90	33	for	for	ADP
geusb-8353	90	34	kriging	krige	VERB
geusb-8353	90	35	does	do	AUX
geusb-8353	90	36	not	not	PART
geusb-8353	90	37	usually	usually	ADV
geusb-8353	90	38	produce	produce	VERB
geusb-8353	90	39	realistic	realistic	ADJ
geusb-8353	90	40	geological	geological	ADJ
geusb-8353	90	41	structures	structure	NOUN
geusb-8353	90	42	.	.	PUNCT
geusb-8353	91	1	in	in	ADP
geusb-8353	91	2	our	our	PRON
geusb-8353	91	3	validation	validation	NOUN
geusb-8353	91	4	example	example	NOUN
geusb-8353	91	5	,	,	PUNCT
geusb-8353	91	6	we	we	PRON
geusb-8353	91	7	employ	employ	VERB
geusb-8353	91	8	the	the	DET
geusb-8353	91	9	ml	ml	NOUN
geusb-8353	91	10	approach	approach	NOUN
geusb-8353	91	11	to	to	PART
geusb-8353	91	12	estimate	estimate	VERB
geusb-8353	91	13	local	local	ADJ
geusb-8353	91	14	values	value	NOUN
geusb-8353	91	15	for	for	ADP
geusb-8353	91	16	the	the	DET
geusb-8353	91	17	range	range	NOUN
geusb-8353	91	18	and	and	CCONJ
geusb-8353	91	19	the	the	DET
geusb-8353	91	20	sill	sill	NOUN
geusb-8353	91	21	and	and	CCONJ
geusb-8353	91	22	then	then	ADV
geusb-8353	91	23	use	use	VERB
geusb-8353	91	24	a	a	DET
geusb-8353	91	25	clustering	clustering	ADJ
geusb-8353	91	26	algorithm	algorithm	NOUN
geusb-8353	91	27	to	to	PART
geusb-8353	91	28	divide	divide	VERB
geusb-8353	91	29	the	the	DET
geusb-8353	91	30	model	model	NOUN
geusb-8353	91	31	into	into	ADP
geusb-8353	91	32	local	local	ADJ
geusb-8353	91	33	regions	region	NOUN
geusb-8353	91	34	with	with	ADP
geusb-8353	91	35	similar	similar	ADJ
geusb-8353	91	36	statistical	statistical	ADJ
geusb-8353	91	37	properties	property	NOUN
geusb-8353	91	38	.	.	PUNCT
geusb-8353	92	1	the	the	DET
geusb-8353	92	2	algorithm	algorithm	NOUN
geusb-8353	92	3	is	be	AUX
geusb-8353	92	4	a	a	DET
geusb-8353	92	5	type	type	NOUN
geusb-8353	92	6	of	of	ADP
geusb-8353	92	7	unsupervised	unsupervised	ADJ
geusb-8353	92	8	classification	classification	NOUN
geusb-8353	92	9	algorithm	algorithm	NOUN
geusb-8353	92	10	known	know	VERB
geusb-8353	92	11	as	as	ADP
geusb-8353	92	12	the	the	DET
geusb-8353	92	13	kohonen	kohonen	PROPN
geusb-8353	92	14	self	self	NOUN
geusb-8353	92	15	-	-	PUNCT
geusb-8353	92	16	organising	organise	VERB
geusb-8353	92	17	map	map	NOUN
geusb-8353	92	18	(	(	PUNCT
geusb-8353	92	19	kohonen	kohonen	PROPN
geusb-8353	92	20	1991	1991	NUM
geusb-8353	92	21	)	)	PUNCT
geusb-8353	92	22	,	,	PUNCT
geusb-8353	92	23	which	which	PRON
geusb-8353	92	24	is	be	AUX
geusb-8353	92	25	featured	feature	VERB
geusb-8353	92	26	as	as	ADP
geusb-8353	92	27	a	a	DET
geusb-8353	92	28	built	build	VERB
geusb-8353	92	29	-	-	PUNCT
geusb-8353	92	30	in	in	ADP
geusb-8353	92	31	function	function	NOUN
geusb-8353	92	32	in	in	ADP
geusb-8353	92	33	matlab	matlab	PROPN
geusb-8353	92	34	.	.	PUNCT
geusb-8353	93	1	with	with	ADP
geusb-8353	93	2	this	this	DET
geusb-8353	93	3	approach	approach	NOUN
geusb-8353	93	4	,	,	PUNCT
geusb-8353	93	5	the	the	DET
geusb-8353	93	6	ml	ml	PROPN
geusb-8353	93	7	algorithm	algorithm	NOUN
geusb-8353	93	8	enables	enable	VERB
geusb-8353	93	9	kriging	krige	VERB
geusb-8353	93	10	with	with	ADP
geusb-8353	93	11	a	a	DET
geusb-8353	93	12	locally	locally	ADV
geusb-8353	93	13	optimised	optimise	VERB
geusb-8353	93	14	semivariogram	semivariogram	NOUN
geusb-8353	93	15	model	model	NOUN
geusb-8353	93	16	,	,	PUNCT
geusb-8353	93	17	which	which	PRON
geusb-8353	93	18	should	should	AUX
geusb-8353	93	19	be	be	AUX
geusb-8353	93	20	better	well	ADJ
geusb-8353	93	21	at	at	ADP
geusb-8353	93	22	handling	handle	VERB
geusb-8353	93	23	non	non	ADJ
geusb-8353	93	24	-	-	ADJ
geusb-8353	93	25	stationary	stationary	ADJ
geusb-8353	93	26	models	model	NOUN
geusb-8353	93	27	than	than	ADP
geusb-8353	93	28	approaches	approach	NOUN
geusb-8353	93	29	with	with	ADP
geusb-8353	93	30	a	a	DET
geusb-8353	93	31	fixed	fix	VERB
geusb-8353	93	32	semivariogram	semivariogram	NOUN
geusb-8353	93	33	model	model	NOUN
geusb-8353	93	34	.	.	PUNCT
geusb-8353	94	1	for	for	ADP
geusb-8353	94	2	the	the	DET
geusb-8353	94	3	validation	validation	NOUN
geusb-8353	94	4	example	example	NOUN
geusb-8353	94	5	,	,	PUNCT
geusb-8353	94	6	we	we	PRON
geusb-8353	94	7	employ	employ	VERB
geusb-8353	94	8	this	this	DET
geusb-8353	94	9	approach	approach	NOUN
geusb-8353	94	10	to	to	ADP
geusb-8353	94	11	the	the	DET
geusb-8353	94	12	first	first	ADJ
geusb-8353	94	13	layer	layer	NOUN
geusb-8353	94	14	in	in	ADP
geusb-8353	94	15	the	the	DET
geusb-8353	94	16	hydrostratigrahic	hydrostratigrahic	ADJ
geusb-8353	94	17	model	model	NOUN
geusb-8353	94	18	on	on	ADP
geusb-8353	94	19	the	the	DET
geusb-8353	94	20	danish	danish	ADJ
geusb-8353	94	21	island	island	NOUN
geusb-8353	94	22	of	of	ADP
geusb-8353	94	23	fyn	fyn	PROPN
geusb-8353	94	24	,	,	PUNCT
geusb-8353	94	25	covering	cover	VERB
geusb-8353	94	26	roughly	roughly	ADV
geusb-8353	94	27	3100	3100	NUM
geusb-8353	94	28	km2	km2	NOUN
geusb-8353	94	29	.	.	PUNCT
geusb-8353	95	1	fig	fig	NOUN
geusb-8353	95	2	.	.	PUNCT
geusb-8353	96	1	2	2	NUM
geusb-8353	96	2	 	 	SPACE
geusb-8353	96	3	the	the	DET
geusb-8353	96	4	panels	panel	NOUN
geusb-8353	96	5	show	show	VERB
geusb-8353	96	6	the	the	DET
geusb-8353	96	7	precision	precision	NOUN
geusb-8353	96	8	and	and	CCONJ
geusb-8353	96	9	accuracy	accuracy	NOUN
geusb-8353	96	10	of	of	ADP
geusb-8353	96	11	the	the	DET
geusb-8353	96	12	ml	ml	PROPN
geusb-8353	96	13	model	model	NOUN
geusb-8353	96	14	in	in	ADP
geusb-8353	96	15	predicting	predict	VERB
geusb-8353	96	16	range	range	NOUN
geusb-8353	96	17	and	and	CCONJ
geusb-8353	96	18	sill	sill	ADJ
geusb-8353	96	19	.	.	PUNCT
geusb-8353	97	1	red	red	ADJ
geusb-8353	97	2	dots	dot	NOUN
geusb-8353	97	3	show	show	VERB
geusb-8353	97	4	values	value	NOUN
geusb-8353	97	5	predicted	predict	VERB
geusb-8353	97	6	using	use	VERB
geusb-8353	97	7	the	the	DET
geusb-8353	97	8	scattered	scatter	VERB
geusb-8353	97	9	points	point	NOUN
geusb-8353	97	10	and	and	CCONJ
geusb-8353	97	11	blue	blue	ADJ
geusb-8353	97	12	dots	dot	NOUN
geusb-8353	97	13	using	use	VERB
geusb-8353	97	14	full	full	ADJ
geusb-8353	97	15	grids	grid	NOUN
geusb-8353	97	16	,	,	PUNCT
geusb-8353	97	17	and	and	CCONJ
geusb-8353	97	18	green	green	ADJ
geusb-8353	97	19	dots	dot	NOUN
geusb-8353	97	20	show	show	VERB
geusb-8353	97	21	the	the	DET
geusb-8353	97	22	estimation	estimation	NOUN
geusb-8353	97	23	of	of	ADP
geusb-8353	97	24	sill	sill	ADV
geusb-8353	97	25	using	use	VERB
geusb-8353	97	26	the	the	DET
geusb-8353	97	27	sample	sample	NOUN
geusb-8353	97	28	variance	variance	NOUN
geusb-8353	97	29	from	from	ADP
geusb-8353	97	30	the	the	DET
geusb-8353	97	31	120	120	NUM
geusb-8353	97	32	scattered	scatter	VERB
geusb-8353	97	33	points	point	NOUN
geusb-8353	97	34	.	.	PUNCT
geusb-8353	98	1	the	the	DET
geusb-8353	98	2	black	black	ADJ
geusb-8353	98	3	trend	trend	NOUN
geusb-8353	98	4	lines	line	NOUN
geusb-8353	98	5	indicate	indicate	VERB
geusb-8353	98	6	where	where	SCONJ
geusb-8353	98	7	points	point	NOUN
geusb-8353	98	8	are	be	AUX
geusb-8353	98	9	in	in	ADP
geusb-8353	98	10	exact	exact	ADJ
geusb-8353	98	11	agreement	agreement	NOUN
geusb-8353	98	12	with	with	ADP
geusb-8353	98	13	the	the	DET
geusb-8353	98	14	true	true	ADJ
geusb-8353	98	15	values	value	NOUN
geusb-8353	98	16	.	.	PUNCT
geusb-8353	99	1	the	the	DET
geusb-8353	99	2	colour	colour	NOUN
geusb-8353	99	3	-	-	PUNCT
geusb-8353	99	4	shaded	shade	VERB
geusb-8353	99	5	regions	region	NOUN
geusb-8353	99	6	highlight	highlight	VERB
geusb-8353	99	7	the	the	DET
geusb-8353	99	8	interval	interval	NOUN
geusb-8353	99	9	within	within	ADP
geusb-8353	99	10	which	which	PRON
geusb-8353	99	11	95	95	NUM
geusb-8353	99	12	%	%	NOUN
geusb-8353	99	13	of	of	ADP
geusb-8353	99	14	predictions	prediction	NOUN
geusb-8353	99	15	are	be	AUX
geusb-8353	99	16	made	make	VERB
geusb-8353	99	17	,	,	PUNCT
geusb-8353	99	18	bounded	bound	VERB
geusb-8353	99	19	by	by	ADP
geusb-8353	99	20	the	the	DET
geusb-8353	99	21	2.5	2.5	NUM
geusb-8353	99	22	percentile	percentile	ADJ
geusb-8353	99	23	(	(	PUNCT
geusb-8353	99	24	lowermost	lowermost	ADJ
geusb-8353	99	25	coloured	coloured	ADJ
geusb-8353	99	26	lines	line	NOUN
geusb-8353	99	27	)	)	PUNCT
geusb-8353	99	28	,	,	PUNCT
geusb-8353	99	29	50	50	NUM
geusb-8353	99	30	percentile	percentile	ADJ
geusb-8353	99	31	(	(	PUNCT
geusb-8353	99	32	median	median	NOUN
geusb-8353	99	33	;	;	PUNCT
geusb-8353	99	34	middle	middle	ADJ
geusb-8353	99	35	coloured	coloured	ADJ
geusb-8353	99	36	lines	line	NOUN
geusb-8353	99	37	)	)	PUNCT
geusb-8353	99	38	and	and	CCONJ
geusb-8353	99	39	97.5	97.5	NUM
geusb-8353	99	40	percentile	percentile	ADJ
geusb-8353	99	41	(	(	PUNCT
geusb-8353	99	42	uppermost	uppermost	ADJ
geusb-8353	99	43	coloured	coloured	ADJ
geusb-8353	99	44	lines	line	NOUN
geusb-8353	99	45	)	)	PUNCT
geusb-8353	99	46	.	.	PUNCT
geusb-8353	100	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	100	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	100	3	falk	falk	NOUN
geusb-8353	100	4	&	&	CCONJ
geusb-8353	100	5	madsen	madsen	PROPN
geusb-8353	100	6	2023	2023	NUM
geusb-8353	100	7	:	:	PUNCT
geusb-8353	100	8	geus	geus	NOUN
geusb-8353	100	9	bulletin	bulletin	NOUN
geusb-8353	100	10	53	53	NUM
geusb-8353	100	11	.	.	PUNCT
geusb-8353	100	12	8353	8353	NUM
geusb-8353	100	13	.	.	PUNCT
geusb-8353	101	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	101	2	5	5	NUM
geusb-8353	101	3	of	of	ADP
geusb-8353	101	4	7	7	NUM
geusb-8353	101	5	www.geusbul	www.geusbul	NOUN
geusb-8353	101	6	let	let	VERB
geusb-8353	101	7	in.org	in.org	ADJ
geusb-8353	101	8	the	the	DET
geusb-8353	101	9	sample	sample	NOUN
geusb-8353	101	10	variance	variance	NOUN
geusb-8353	101	11	is	be	AUX
geusb-8353	101	12	used	use	VERB
geusb-8353	101	13	to	to	PART
geusb-8353	101	14	estimate	estimate	VERB
geusb-8353	101	15	the	the	DET
geusb-8353	101	16	sill	sill	NOUN
geusb-8353	101	17	in	in	ADP
geusb-8353	101	18	this	this	DET
geusb-8353	101	19	example	example	NOUN
geusb-8353	101	20	because	because	SCONJ
geusb-8353	101	21	an	an	DET
geusb-8353	101	22	unbiased	unbiased	ADJ
geusb-8353	101	23	estimation	estimation	NOUN
geusb-8353	101	24	is	be	AUX
geusb-8353	101	25	prioritised	prioritise	VERB
geusb-8353	101	26	over	over	ADP
geusb-8353	101	27	a	a	DET
geusb-8353	101	28	precise	precise	ADJ
geusb-8353	101	29	estimation	estimation	NOUN
geusb-8353	101	30	for	for	ADP
geusb-8353	101	31	the	the	DET
geusb-8353	101	32	purpose	purpose	NOUN
geusb-8353	101	33	of	of	ADP
geusb-8353	101	34	clustering	clustering	NOUN
geusb-8353	101	35	.	.	PUNCT
geusb-8353	102	1	as	as	ADP
geusb-8353	102	2	such	such	ADJ
geusb-8353	102	3	,	,	PUNCT
geusb-8353	102	4	the	the	DET
geusb-8353	102	5	example	example	NOUN
geusb-8353	102	6	focuses	focus	VERB
geusb-8353	102	7	on	on	ADP
geusb-8353	102	8	the	the	DET
geusb-8353	102	9	predicted	predict	VERB
geusb-8353	102	10	ranges	range	NOUN
geusb-8353	102	11	and	and	CCONJ
geusb-8353	102	12	the	the	DET
geusb-8353	102	13	final	final	ADJ
geusb-8353	102	14	clustering	clustering	NOUN
geusb-8353	102	15	.	.	PUNCT
geusb-8353	103	1	the	the	DET
geusb-8353	103	2	top	top	ADJ
geusb-8353	103	3	left	left	ADJ
geusb-8353	103	4	panel	panel	NOUN
geusb-8353	103	5	in	in	ADP
geusb-8353	103	6	fig	fig	NOUN
geusb-8353	103	7	.	.	PUNCT
geusb-8353	104	1	3	3	NUM
geusb-8353	104	2	shows	show	VERB
geusb-8353	104	3	the	the	DET
geusb-8353	104	4	ranges	range	NOUN
geusb-8353	104	5	predicted	predict	VERB
geusb-8353	104	6	with	with	ADP
geusb-8353	104	7	a	a	DET
geusb-8353	104	8	31	31	NUM
geusb-8353	104	9	×	×	NOUN
geusb-8353	104	10	31	31	NUM
geusb-8353	104	11	sliding	slide	VERB
geusb-8353	104	12	window	window	NOUN
geusb-8353	104	13	,	,	PUNCT
geusb-8353	104	14	whilst	whilst	SCONJ
geusb-8353	104	15	the	the	DET
geusb-8353	104	16	top	top	ADJ
geusb-8353	104	17	right	right	ADJ
geusb-8353	104	18	panel	panel	NOUN
geusb-8353	104	19	shows	show	VERB
geusb-8353	104	20	the	the	DET
geusb-8353	104	21	subsequent	subsequent	ADJ
geusb-8353	104	22	clustering	clustering	NOUN
geusb-8353	104	23	result	result	NOUN
geusb-8353	104	24	.	.	PUNCT
geusb-8353	105	1	figure	figure	VERB
geusb-8353	105	2	4	4	NUM
geusb-8353	105	3	illustrates	illustrate	VERB
geusb-8353	105	4	how	how	SCONJ
geusb-8353	105	5	the	the	DET
geusb-8353	105	6	ml	ml	PROPN
geusb-8353	105	7	algorithm	algorithm	NOUN
geusb-8353	105	8	identifies	identify	VERB
geusb-8353	105	9	areas	area	NOUN
geusb-8353	105	10	of	of	ADP
geusb-8353	105	11	low	low	ADJ
geusb-8353	105	12	range	range	NOUN
geusb-8353	105	13	where	where	SCONJ
geusb-8353	105	14	the	the	DET
geusb-8353	105	15	layer	layer	NOUN
geusb-8353	105	16	has	have	VERB
geusb-8353	105	17	more	more	ADJ
geusb-8353	105	18	high	high	ADJ
geusb-8353	105	19	-	-	PUNCT
geusb-8353	105	20	frequency	frequency	NOUN
geusb-8353	105	21	variation	variation	NOUN
geusb-8353	105	22	and	and	CCONJ
geusb-8353	105	23	areas	area	NOUN
geusb-8353	105	24	of	of	ADP
geusb-8353	105	25	high	high	ADJ
geusb-8353	105	26	range	range	NOUN
geusb-8353	105	27	where	where	SCONJ
geusb-8353	105	28	the	the	DET
geusb-8353	105	29	layer	layer	NOUN
geusb-8353	105	30	resembles	resemble	VERB
geusb-8353	105	31	more	more	ADJ
geusb-8353	105	32	of	of	ADP
geusb-8353	105	33	a	a	DET
geusb-8353	105	34	smooth	smooth	ADJ
geusb-8353	105	35	curve	curve	NOUN
geusb-8353	105	36	.	.	PUNCT
geusb-8353	106	1	of	of	ADP
geusb-8353	106	2	course	course	NOUN
geusb-8353	106	3	,	,	PUNCT
geusb-8353	106	4	this	this	PRON
geusb-8353	106	5	should	should	AUX
geusb-8353	106	6	be	be	AUX
geusb-8353	106	7	seen	see	VERB
geusb-8353	106	8	in	in	ADP
geusb-8353	106	9	the	the	DET
geusb-8353	106	10	context	context	NOUN
geusb-8353	106	11	of	of	ADP
geusb-8353	106	12	the	the	DET
geusb-8353	106	13	chosen	choose	VERB
geusb-8353	106	14	window	window	NOUN
geusb-8353	106	15	size	size	NOUN
geusb-8353	106	16	,	,	PUNCT
geusb-8353	106	17	which	which	PRON
geusb-8353	106	18	is	be	AUX
geusb-8353	106	19	about	about	ADV
geusb-8353	106	20	3100	3100	NUM
geusb-8353	106	21	m	m	NOUN
geusb-8353	106	22	,	,	PUNCT
geusb-8353	106	23	and	and	CCONJ
geusb-8353	106	24	larger	large	ADJ
geusb-8353	106	25	ranges	range	NOUN
geusb-8353	106	26	than	than	SCONJ
geusb-8353	106	27	this	this	PRON
geusb-8353	106	28	can	can	AUX
geusb-8353	106	29	not	not	PART
geusb-8353	106	30	be	be	AUX
geusb-8353	106	31	resolved	resolve	VERB
geusb-8353	106	32	.	.	PUNCT
geusb-8353	107	1	this	this	DET
geusb-8353	107	2	limitation	limitation	NOUN
geusb-8353	107	3	should	should	AUX
geusb-8353	107	4	not	not	PART
geusb-8353	107	5	pose	pose	VERB
geusb-8353	107	6	a	a	DET
geusb-8353	107	7	problem	problem	NOUN
geusb-8353	107	8	since	since	SCONJ
geusb-8353	107	9	any	any	DET
geusb-8353	107	10	subsequent	subsequent	ADJ
geusb-8353	107	11	kriging	kriging	NOUN
geusb-8353	107	12	and	and	CCONJ
geusb-8353	107	13	simulation	simulation	NOUN
geusb-8353	107	14	will	will	AUX
geusb-8353	107	15	be	be	AUX
geusb-8353	107	16	modelling	model	VERB
geusb-8353	107	17	the	the	DET
geusb-8353	107	18	residual	residual	ADJ
geusb-8353	107	19	around	around	ADP
geusb-8353	107	20	the	the	DET
geusb-8353	107	21	sliding	slide	VERB
geusb-8353	107	22	mean	mean	NOUN
geusb-8353	107	23	from	from	ADP
geusb-8353	107	24	the	the	DET
geusb-8353	107	25	layer	layer	NOUN
geusb-8353	107	26	using	use	VERB
geusb-8353	107	27	the	the	DET
geusb-8353	107	28	same	same	ADJ
geusb-8353	107	29	window	window	NOUN
geusb-8353	107	30	.	.	PUNCT
geusb-8353	108	1	the	the	DET
geusb-8353	108	2	example	example	NOUN
geusb-8353	108	3	layer	layer	NOUN
geusb-8353	108	4	shown	show	VERB
geusb-8353	108	5	here	here	ADV
geusb-8353	108	6	contains	contain	VERB
geusb-8353	108	7	497	497	NUM
geusb-8353	108	8	  	  	SPACE
geusb-8353	108	9	369	369	NUM
geusb-8353	108	10	individual	individual	ADJ
geusb-8353	108	11	grid	grid	NOUN
geusb-8353	108	12	cells	cell	NOUN
geusb-8353	108	13	,	,	PUNCT
geusb-8353	108	14	which	which	PRON
geusb-8353	108	15	means	mean	VERB
geusb-8353	108	16	that	that	SCONJ
geusb-8353	108	17	the	the	DET
geusb-8353	108	18	machine	machine	NOUN
geusb-8353	108	19	learning	learn	VERB
geusb-8353	108	20	algorithm	algorithm	NOUN
geusb-8353	108	21	can	can	AUX
geusb-8353	108	22	complete	complete	VERB
geusb-8353	108	23	the	the	DET
geusb-8353	108	24	semivariance	semivariance	NOUN
geusb-8353	108	25	model	model	NOUN
geusb-8353	108	26	estimation	estimation	NOUN
geusb-8353	108	27	in	in	ADP
geusb-8353	108	28	about	about	ADV
geusb-8353	108	29	1	1	NUM
geusb-8353	108	30	h	h	NOUN
geusb-8353	108	31	and	and	CCONJ
geusb-8353	108	32	15	15	NUM
geusb-8353	108	33	min	min	NOUN
geusb-8353	108	34	,	,	PUNCT
geusb-8353	108	35	given	give	VERB
geusb-8353	108	36	a	a	DET
geusb-8353	108	37	computation	computation	NOUN
geusb-8353	108	38	rate	rate	NOUN
geusb-8353	108	39	of	of	ADP
geusb-8353	108	40	111	111	NUM
geusb-8353	108	41	models	model	NOUN
geusb-8353	108	42	per	per	ADP
geusb-8353	108	43	second	second	NOUN
geusb-8353	108	44	.	.	PUNCT
geusb-8353	109	1	meanwhile	meanwhile	ADV
geusb-8353	109	2	,	,	PUNCT
geusb-8353	109	3	the	the	DET
geusb-8353	109	4	traditional	traditional	ADJ
geusb-8353	109	5	semivariogram	semivariogram	NOUN
geusb-8353	109	6	analysis	analysis	NOUN
geusb-8353	109	7	takes	take	VERB
geusb-8353	109	8	about	about	ADV
geusb-8353	109	9	80	80	NUM
geusb-8353	109	10	h	h	NOUN
geusb-8353	109	11	to	to	PART
geusb-8353	109	12	do	do	VERB
geusb-8353	109	13	the	the	DET
geusb-8353	109	14	same	same	ADJ
geusb-8353	109	15	,	,	PUNCT
geusb-8353	109	16	given	give	VERB
geusb-8353	109	17	a	a	DET
geusb-8353	109	18	rate	rate	NOUN
geusb-8353	109	19	of	of	ADP
geusb-8353	109	20	1.72	1.72	NUM
geusb-8353	109	21	models	model	NOUN
geusb-8353	109	22	per	per	ADP
geusb-8353	109	23	second	second	ADJ
geusb-8353	109	24	.	.	PUNCT
geusb-8353	110	1	discussion	discussion	NOUN
geusb-8353	110	2	and	and	CCONJ
geusb-8353	110	3	outlook	outlook	NOUN
geusb-8353	110	4	in	in	ADP
geusb-8353	110	5	a	a	DET
geusb-8353	110	6	subsurface	subsurface	NOUN
geusb-8353	110	7	model	model	NOUN
geusb-8353	110	8	with	with	ADP
geusb-8353	110	9	a	a	DET
geusb-8353	110	10	large	large	ADJ
geusb-8353	110	11	spatial	spatial	ADJ
geusb-8353	110	12	extent	extent	NOUN
geusb-8353	110	13	,	,	PUNCT
geusb-8353	110	14	the	the	DET
geusb-8353	110	15	assumption	assumption	NOUN
geusb-8353	110	16	of	of	ADP
geusb-8353	110	17	stationarity	stationarity	NOUN
geusb-8353	110	18	in	in	ADP
geusb-8353	110	19	the	the	DET
geusb-8353	110	20	subsurface	subsurface	NOUN
geusb-8353	110	21	properties	property	NOUN
geusb-8353	110	22	breaks	break	VERB
geusb-8353	110	23	down	down	ADP
geusb-8353	110	24	.	.	PUNCT
geusb-8353	111	1	to	to	PART
geusb-8353	111	2	do	do	VERB
geusb-8353	111	3	proper	proper	ADJ
geusb-8353	111	4	geostatistical	geostatistical	ADJ
geusb-8353	111	5	modelling	modelling	NOUN
geusb-8353	111	6	in	in	ADP
geusb-8353	111	7	such	such	DET
geusb-8353	111	8	a	a	DET
geusb-8353	111	9	case	case	NOUN
geusb-8353	111	10	,	,	PUNCT
geusb-8353	111	11	non	non	ADJ
geusb-8353	111	12	-	-	ADJ
geusb-8353	111	13	stationarity	stationarity	NOUN
geusb-8353	111	14	must	must	AUX
geusb-8353	111	15	be	be	AUX
geusb-8353	111	16	considered	consider	VERB
geusb-8353	111	17	(	(	PUNCT
geusb-8353	111	18	higdon	higdon	PROPN
geusb-8353	111	19	et	et	PROPN
geusb-8353	111	20	al	al	PROPN
geusb-8353	111	21	.	.	PROPN
geusb-8353	111	22	2022	2022	NUM
geusb-8353	111	23	)	)	PUNCT
geusb-8353	111	24	.	.	PUNCT
geusb-8353	112	1	non	non	ADJ
geusb-8353	112	2	-	-	ADJ
geusb-8353	112	3	stationarity	stationarity	NOUN
geusb-8353	112	4	can	can	AUX
geusb-8353	112	5	be	be	AUX
geusb-8353	112	6	modelled	model	VERB
geusb-8353	112	7	by	by	ADP
geusb-8353	112	8	introducing	introduce	VERB
geusb-8353	112	9	locally	locally	ADV
geusb-8353	112	10	varying	vary	VERB
geusb-8353	112	11	anisotropy	anisotropy	NOUN
geusb-8353	112	12	(	(	PUNCT
geusb-8353	112	13	e.g.	e.g.	ADV
geusb-8353	112	14	boisvert	boisvert	PROPN
geusb-8353	112	15	&	&	CCONJ
geusb-8353	112	16	deutsch	deutsch	NOUN
geusb-8353	112	17	2011	2011	NUM
geusb-8353	112	18	;	;	PUNCT
geusb-8353	112	19	bongajum	bongajum	NOUN
geusb-8353	112	20	et	et	PROPN
geusb-8353	112	21	al	al	PROPN
geusb-8353	112	22	.	.	PROPN
geusb-8353	112	23	2013	2013	NUM
geusb-8353	112	24	;	;	PUNCT
geusb-8353	112	25	pereira	pereira	PROPN
geusb-8353	112	26	et	et	PROPN
geusb-8353	112	27	al	al	PROPN
geusb-8353	112	28	.	.	PROPN
geusb-8353	112	29	2023	2023	NUM
geusb-8353	112	30	)	)	PUNCT
geusb-8353	112	31	,	,	PUNCT
geusb-8353	112	32	but	but	CCONJ
geusb-8353	112	33	these	these	DET
geusb-8353	112	34	methods	method	NOUN
geusb-8353	112	35	can	can	AUX
geusb-8353	112	36	be	be	AUX
geusb-8353	112	37	computationally	computationally	ADV
geusb-8353	112	38	challenging	challenge	VERB
geusb-8353	112	39	for	for	ADP
geusb-8353	112	40	large	large	ADJ
geusb-8353	112	41	models	model	NOUN
geusb-8353	112	42	.	.	PUNCT
geusb-8353	113	1	thus	thus	ADV
geusb-8353	113	2	,	,	PUNCT
geusb-8353	113	3	practical	practical	ADJ
geusb-8353	113	4	tools	tool	NOUN
geusb-8353	113	5	needed	need	VERB
geusb-8353	113	6	for	for	ADP
geusb-8353	113	7	estimating	estimate	VERB
geusb-8353	113	8	the	the	DET
geusb-8353	113	9	non	non	ADJ
geusb-8353	113	10	-	-	NOUN
geusb-8353	113	11	stationarity	stationarity	NOUN
geusb-8353	113	12	are	be	AUX
geusb-8353	113	13	currently	currently	ADV
geusb-8353	113	14	sparse	sparse	ADJ
geusb-8353	113	15	and	and	CCONJ
geusb-8353	113	16	not	not	PART
geusb-8353	113	17	easily	easily	ADV
geusb-8353	113	18	deployed	deploy	VERB
geusb-8353	113	19	for	for	ADP
geusb-8353	113	20	practitioners	practitioner	NOUN
geusb-8353	113	21	(	(	PUNCT
geusb-8353	113	22	madsen	madsen	PROPN
geusb-8353	113	23	et	et	PROPN
geusb-8353	113	24	al	al	PROPN
geusb-8353	113	25	.	.	PROPN
geusb-8353	113	26	2020	2020	NUM
geusb-8353	113	27	)	)	PUNCT
geusb-8353	113	28	.	.	PUNCT
geusb-8353	114	1	here	here	ADV
geusb-8353	114	2	,	,	PUNCT
geusb-8353	114	3	we	we	PRON
geusb-8353	114	4	briefly	briefly	ADV
geusb-8353	114	5	presented	present	VERB
geusb-8353	114	6	a	a	DET
geusb-8353	114	7	computationally	computationally	ADV
geusb-8353	114	8	efficient	efficient	ADJ
geusb-8353	114	9	ml	ml	NOUN
geusb-8353	114	10	-	-	PUNCT
geusb-8353	114	11	based	base	VERB
geusb-8353	114	12	method	method	NOUN
geusb-8353	114	13	that	that	PRON
geusb-8353	114	14	can	can	AUX
geusb-8353	114	15	infer	infer	VERB
geusb-8353	114	16	gaussian	gaussian	ADJ
geusb-8353	114	17	properties	property	NOUN
geusb-8353	114	18	from	from	ADP
geusb-8353	114	19	a	a	DET
geusb-8353	114	20	stratigraphic	stratigraphic	ADJ
geusb-8353	114	21	layer	layer	NOUN
geusb-8353	114	22	model	model	NOUN
geusb-8353	114	23	,	,	PUNCT
geusb-8353	114	24	adding	add	VERB
geusb-8353	114	25	a	a	DET
geusb-8353	114	26	new	new	ADJ
geusb-8353	114	27	tool	tool	NOUN
geusb-8353	114	28	to	to	ADP
geusb-8353	114	29	the	the	DET
geusb-8353	114	30	geostatistician	geostatistician	PROPN
geusb-8353	114	31	’s	’s	PART
geusb-8353	114	32	toolbox	toolbox	NOUN
geusb-8353	114	33	to	to	PART
geusb-8353	114	34	solve	solve	VERB
geusb-8353	114	35	issues	issue	NOUN
geusb-8353	114	36	of	of	ADP
geusb-8353	114	37	non	non	ADJ
geusb-8353	114	38	-	-	NOUN
geusb-8353	114	39	stationarity	stationarity	NOUN
geusb-8353	114	40	.	.	PUNCT
geusb-8353	115	1	during	during	ADP
geusb-8353	115	2	testing	testing	NOUN
geusb-8353	115	3	,	,	PUNCT
geusb-8353	115	4	several	several	ADJ
geusb-8353	115	5	different	different	ADJ
geusb-8353	115	6	ml	ml	NOUN
geusb-8353	115	7	approaches	approach	NOUN
geusb-8353	115	8	were	be	AUX
geusb-8353	115	9	tried	try	VERB
geusb-8353	115	10	,	,	PUNCT
geusb-8353	115	11	including	include	VERB
geusb-8353	115	12	regression	regression	NOUN
geusb-8353	115	13	trees	tree	NOUN
geusb-8353	115	14	,	,	PUNCT
geusb-8353	115	15	random	random	ADJ
geusb-8353	115	16	forest	forest	NOUN
geusb-8353	115	17	and	and	CCONJ
geusb-8353	115	18	a	a	DET
geusb-8353	115	19	classical	classical	ADJ
geusb-8353	115	20	nn	nn	NOUN
geusb-8353	115	21	,	,	PUNCT
geusb-8353	115	22	but	but	CCONJ
geusb-8353	115	23	the	the	DET
geusb-8353	115	24	deployment	deployment	NOUN
geusb-8353	115	25	and	and	CCONJ
geusb-8353	115	26	adoption	adoption	NOUN
geusb-8353	115	27	of	of	ADP
geusb-8353	115	28	a	a	DET
geusb-8353	115	29	cnn	cnn	PROPN
geusb-8353	115	30	were	be	AUX
geusb-8353	115	31	the	the	DET
geusb-8353	115	32	most	most	ADV
geusb-8353	115	33	successful	successful	ADJ
geusb-8353	115	34	.	.	PUNCT
geusb-8353	116	1	it	it	PRON
geusb-8353	116	2	is	be	AUX
geusb-8353	116	3	known	know	VERB
geusb-8353	116	4	that	that	SCONJ
geusb-8353	116	5	neural	neural	ADJ
geusb-8353	116	6	networks	network	NOUN
geusb-8353	116	7	in	in	ADP
geusb-8353	116	8	general	general	ADJ
geusb-8353	116	9	are	be	AUX
geusb-8353	116	10	universal	universal	ADJ
geusb-8353	116	11	approximators	approximator	NOUN
geusb-8353	116	12	that	that	PRON
geusb-8353	116	13	can	can	AUX
geusb-8353	116	14	approximate	approximate	VERB
geusb-8353	116	15	any	any	DET
geusb-8353	116	16	continuous	continuous	ADJ
geusb-8353	116	17	lebesgue	lebesgue	NOUN
geusb-8353	116	18	integrable	integrable	ADJ
geusb-8353	116	19	function	function	NOUN
geusb-8353	116	20	.	.	PUNCT
geusb-8353	117	1	this	this	PRON
geusb-8353	117	2	was	be	AUX
geusb-8353	117	3	proven	prove	VERB
geusb-8353	117	4	for	for	ADP
geusb-8353	117	5	networks	network	NOUN
geusb-8353	117	6	with	with	ADP
geusb-8353	117	7	a	a	DET
geusb-8353	117	8	fixed	fix	VERB
geusb-8353	117	9	number	number	NOUN
geusb-8353	117	10	of	of	ADP
geusb-8353	117	11	hidden	hidden	ADJ
geusb-8353	117	12	layers	layer	NOUN
geusb-8353	117	13	and	and	CCONJ
geusb-8353	117	14	an	an	DET
geusb-8353	117	15	arbitrary	arbitrary	ADJ
geusb-8353	117	16	number	number	NOUN
geusb-8353	117	17	of	of	ADP
geusb-8353	117	18	neurons	neuron	NOUN
geusb-8353	117	19	,	,	PUNCT
geusb-8353	117	20	also	also	ADV
geusb-8353	117	21	known	know	VERB
geusb-8353	117	22	as	as	ADP
geusb-8353	117	23	the	the	DET
geusb-8353	117	24	arbitrary	arbitrary	ADJ
geusb-8353	117	25	width	width	ADJ
geusb-8353	117	26	case	case	NOUN
geusb-8353	117	27	(	(	PUNCT
geusb-8353	117	28	hornik	hornik	X
geusb-8353	117	29	1991	1991	NUM
geusb-8353	117	30	)	)	PUNCT
geusb-8353	117	31	.	.	PUNCT
geusb-8353	118	1	recently	recently	ADV
geusb-8353	118	2	,	,	PUNCT
geusb-8353	118	3	it	it	PRON
geusb-8353	118	4	was	be	AUX
geusb-8353	118	5	also	also	ADV
geusb-8353	118	6	proven	prove	VERB
geusb-8353	118	7	for	for	ADP
geusb-8353	118	8	relu	relu	NOUN
geusb-8353	118	9	nns	nn	NOUN
geusb-8353	118	10	with	with	ADP
geusb-8353	118	11	a	a	DET
geusb-8353	118	12	fixed	fix	VERB
geusb-8353	118	13	synthetic	synthetic	ADJ
geusb-8353	118	14	example	example	NOUN
geusb-8353	118	15	10	10	NUM
geusb-8353	118	16	20	20	NUM
geusb-8353	118	17	30	30	NUM
geusb-8353	118	18	y	y	NOUN
geusb-8353	119	1	[	[	X
geusb-8353	119	2	m	m	X
geusb-8353	119	3	]	]	X
geusb-8353	119	4	semivariance	semivariance	NOUN
geusb-8353	120	1	[	[	X
geusb-8353	120	2	m2	m2	X
geusb-8353	120	3	]	]	X
geusb-8353	120	4	synthetic	synthetic	ADJ
geusb-8353	120	5	example	example	NOUN
geusb-8353	120	6	semivariance	semivariance	NOUN
geusb-8353	121	1	[	[	X
geusb-8353	121	2	m2	m2	PROPN
geusb-8353	121	3	]	]	X
geusb-8353	121	4	10	10	NUM
geusb-8353	121	5	20	20	NUM
geusb-8353	121	6	30	30	NUM
geusb-8353	121	7	y	y	NOUN
geusb-8353	122	1	[	[	X
geusb-8353	122	2	m	m	X
geusb-8353	122	3	]	]	X
geusb-8353	122	4	10	10	NUM
geusb-8353	122	5	20	20	NUM
geusb-8353	122	6	30	30	NUM
geusb-8353	122	7	y	y	NOUN
geusb-8353	123	1	[	[	X
geusb-8353	123	2	m	m	X
geusb-8353	123	3	]	]	X
geusb-8353	123	4	10	10	NUM
geusb-8353	123	5	20	20	NUM
geusb-8353	123	6	30	30	NUM
geusb-8353	123	7	x	x	SYM
geusb-8353	124	1	[	[	X
geusb-8353	124	2	m	m	X
geusb-8353	124	3	]	]	X
geusb-8353	124	4	10	10	NUM
geusb-8353	124	5	20	20	NUM
geusb-8353	124	6	30	30	NUM
geusb-8353	124	7	y	y	PROPN
geusb-8353	125	1	[	[	X
geusb-8353	125	2	m	m	X
geusb-8353	125	3	]	]	X
geusb-8353	125	4	0	0	NUM
geusb-8353	125	5	1000	1000	NUM
geusb-8353	125	6	2000	2000	NUM
geusb-8353	125	7	3000	3000	NUM
geusb-8353	125	8	distance	distance	NOUN
geusb-8353	125	9	[	[	X
geusb-8353	125	10	m	m	X
geusb-8353	125	11	]	]	X
geusb-8353	125	12	10	10	NUM
geusb-8353	125	13	20	20	NUM
geusb-8353	125	14	30	30	NUM
geusb-8353	125	15	x	x	SYM
geusb-8353	126	1	[	[	X
geusb-8353	126	2	m	m	X
geusb-8353	126	3	]	]	X
geusb-8353	126	4	0	0	NUM
geusb-8353	126	5	1000	1000	NUM
geusb-8353	126	6	2000	2000	NUM
geusb-8353	126	7	3000	3000	NUM
geusb-8353	126	8	distance	distance	NOUN
geusb-8353	126	9	[	[	X
geusb-8353	126	10	m	m	X
geusb-8353	126	11	]	]	X
geusb-8353	126	12	synthetic	synthetic	ADJ
geusb-8353	126	13	data	datum	NOUN
geusb-8353	126	14	data	data	PROPN
geusb-8353	126	15	true	true	ADJ
geusb-8353	126	16	model	model	NOUN
geusb-8353	126	17	ml	ml	PROPN
geusb-8353	126	18	model	model	PROPN
geusb-8353	126	19	fitted	fit	VERB
geusb-8353	126	20	model	model	NOUN
geusb-8353	126	21	fig	fig	NOUN
geusb-8353	126	22	.	.	PUNCT
geusb-8353	127	1	3	3	NUM
geusb-8353	127	2	 	 	SPACE
geusb-8353	127	3	synthetic	synthetic	ADJ
geusb-8353	127	4	examples	example	NOUN
geusb-8353	127	5	:	:	PUNCT
geusb-8353	127	6	8	8	NUM
geusb-8353	127	7	realisations	realisation	NOUN
geusb-8353	127	8	are	be	AUX
geusb-8353	127	9	shown	show	VERB
geusb-8353	127	10	–	–	PUNCT
geusb-8353	127	11	each	each	PRON
geusb-8353	127	12	of	of	ADP
geusb-8353	127	13	their	their	PRON
geusb-8353	127	14	own	own	ADJ
geusb-8353	127	15	gaussian	gaussian	ADJ
geusb-8353	127	16	semivariogram	semivariogram	NOUN
geusb-8353	127	17	model	model	NOUN
geusb-8353	127	18	with	with	ADP
geusb-8353	127	19	some	some	DET
geusb-8353	127	20	component	component	NOUN
geusb-8353	127	21	of	of	ADP
geusb-8353	127	22	noise	noise	NOUN
geusb-8353	127	23	.	.	PUNCT
geusb-8353	128	1	the	the	DET
geusb-8353	128	2	semivariance	semivariance	NOUN
geusb-8353	128	3	models	model	NOUN
geusb-8353	128	4	show	show	VERB
geusb-8353	128	5	a	a	DET
geusb-8353	128	6	high	high	ADJ
geusb-8353	128	7	level	level	NOUN
geusb-8353	128	8	of	of	ADP
geusb-8353	128	9	accuracy	accuracy	NOUN
geusb-8353	128	10	for	for	ADP
geusb-8353	128	11	the	the	DET
geusb-8353	128	12	traditional	traditional	ADJ
geusb-8353	128	13	fitting	fitting	ADJ
geusb-8353	128	14	approach	approach	NOUN
geusb-8353	128	15	as	as	ADV
geusb-8353	128	16	well	well	ADV
geusb-8353	128	17	as	as	ADP
geusb-8353	128	18	the	the	DET
geusb-8353	128	19	ml	ml	NOUN
geusb-8353	128	20	approach	approach	NOUN
geusb-8353	128	21	.	.	PUNCT
geusb-8353	129	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	129	2	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	129	3	falk	falk	NOUN
geusb-8353	129	4	&	&	CCONJ
geusb-8353	129	5	madsen	madsen	PROPN
geusb-8353	129	6	2023	2023	NUM
geusb-8353	129	7	:	:	PUNCT
geusb-8353	129	8	geus	geus	NOUN
geusb-8353	129	9	bulletin	bulletin	NOUN
geusb-8353	129	10	53	53	NUM
geusb-8353	129	11	.	.	PUNCT
geusb-8353	129	12	8353	8353	NUM
geusb-8353	129	13	.	.	PUNCT
geusb-8353	130	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	130	2	6	6	NUM
geusb-8353	130	3	of	of	ADP
geusb-8353	130	4	7	7	NUM
geusb-8353	130	5	www.geusbul	www.geusbul	NOUN
geusb-8353	130	6	let	let	VERB
geusb-8353	130	7	in.org	in.org	ADJ
geusb-8353	130	8	number	number	NOUN
geusb-8353	130	9	of	of	ADP
geusb-8353	130	10	neurons	neuron	NOUN
geusb-8353	130	11	(	(	PUNCT
geusb-8353	130	12	fixed	fix	VERB
geusb-8353	130	13	width	width	NOUN
geusb-8353	130	14	)	)	PUNCT
geusb-8353	130	15	and	and	CCONJ
geusb-8353	130	16	an	an	DET
geusb-8353	130	17	arbitrary	arbitrary	ADJ
geusb-8353	130	18	number	number	NOUN
geusb-8353	130	19	of	of	ADP
geusb-8353	130	20	hidden	hidden	ADJ
geusb-8353	130	21	layers	layer	NOUN
geusb-8353	130	22	,	,	PUNCT
geusb-8353	130	23	also	also	ADV
geusb-8353	130	24	known	know	VERB
geusb-8353	130	25	as	as	ADP
geusb-8353	130	26	the	the	DET
geusb-8353	130	27	arbitrary	arbitrary	ADJ
geusb-8353	130	28	depth	depth	NOUN
geusb-8353	130	29	case	case	NOUN
geusb-8353	130	30	(	(	PUNCT
geusb-8353	130	31	zhou	zhou	X
geusb-8353	130	32	et	et	PROPN
geusb-8353	130	33	al	al	PROPN
geusb-8353	130	34	.	.	PROPN
geusb-8353	130	35	2017	2017	NUM
geusb-8353	130	36	)	)	PUNCT
geusb-8353	130	37	.	.	PUNCT
geusb-8353	131	1	in	in	ADP
geusb-8353	131	2	the	the	DET
geusb-8353	131	3	presented	present	VERB
geusb-8353	131	4	use	use	NOUN
geusb-8353	131	5	-	-	PUNCT
geusb-8353	131	6	case	case	NOUN
geusb-8353	131	7	,	,	PUNCT
geusb-8353	131	8	the	the	DET
geusb-8353	131	9	cnn	cnn	PROPN
geusb-8353	131	10	probably	probably	ADV
geusb-8353	131	11	produced	produce	VERB
geusb-8353	131	12	better	well	ADJ
geusb-8353	131	13	estimates	estimate	NOUN
geusb-8353	131	14	compared	compare	VERB
geusb-8353	131	15	to	to	ADP
geusb-8353	131	16	the	the	DET
geusb-8353	131	17	other	other	ADJ
geusb-8353	131	18	ml	ml	NOUN
geusb-8353	131	19	approaches	approach	NOUN
geusb-8353	131	20	because	because	SCONJ
geusb-8353	131	21	the	the	DET
geusb-8353	131	22	convolution	convolution	NOUN
geusb-8353	131	23	of	of	ADP
geusb-8353	131	24	different	different	ADJ
geusb-8353	131	25	layers	layer	NOUN
geusb-8353	131	26	makes	make	VERB
geusb-8353	131	27	the	the	DET
geusb-8353	131	28	cnn	cnn	NOUN
geusb-8353	131	29	better	well	ADV
geusb-8353	131	30	at	at	ADP
geusb-8353	131	31	analysing	analyse	VERB
geusb-8353	131	32	the	the	DET
geusb-8353	131	33	spatial	spatial	ADJ
geusb-8353	131	34	information	information	NOUN
geusb-8353	131	35	in	in	ADP
geusb-8353	131	36	the	the	DET
geusb-8353	131	37	training	training	NOUN
geusb-8353	131	38	data	datum	NOUN
geusb-8353	131	39	.	.	PUNCT
geusb-8353	132	1	with	with	ADP
geusb-8353	132	2	the	the	DET
geusb-8353	132	3	rapid	rapid	ADJ
geusb-8353	132	4	development	development	NOUN
geusb-8353	132	5	in	in	ADP
geusb-8353	132	6	ml	ml	ADP
geusb-8353	132	7	algorithms	algorithm	NOUN
geusb-8353	132	8	,	,	PUNCT
geusb-8353	132	9	the	the	DET
geusb-8353	132	10	cnn	cnn	PROPN
geusb-8353	132	11	might	might	AUX
geusb-8353	132	12	soon	soon	ADV
geusb-8353	132	13	be	be	AUX
geusb-8353	132	14	outperformed	outperform	VERB
geusb-8353	132	15	;	;	PUNCT
geusb-8353	132	16	however	however	ADV
geusb-8353	132	17	,	,	PUNCT
geusb-8353	132	18	the	the	DET
geusb-8353	132	19	methodology	methodology	NOUN
geusb-8353	132	20	of	of	ADP
geusb-8353	132	21	training	train	VERB
geusb-8353	132	22	the	the	DET
geusb-8353	132	23	ml	ml	PROPN
geusb-8353	132	24	model	model	NOUN
geusb-8353	132	25	to	to	PART
geusb-8353	132	26	recognise	recognise	VERB
geusb-8353	132	27	gaussian	gaussian	ADJ
geusb-8353	132	28	covariances	covariance	NOUN
geusb-8353	132	29	from	from	ADP
geusb-8353	132	30	point	point	NOUN
geusb-8353	132	31	data	datum	NOUN
geusb-8353	132	32	does	do	AUX
geusb-8353	132	33	not	not	PART
geusb-8353	132	34	change	change	VERB
geusb-8353	132	35	but	but	CCONJ
geusb-8353	132	36	can	can	AUX
geusb-8353	132	37	only	only	ADV
geusb-8353	132	38	improve	improve	VERB
geusb-8353	132	39	its	its	PRON
geusb-8353	132	40	precision	precision	NOUN
geusb-8353	132	41	with	with	ADP
geusb-8353	132	42	new	new	ADJ
geusb-8353	132	43	algorithms	algorithm	NOUN
geusb-8353	132	44	.	.	PUNCT
geusb-8353	133	1	this	this	PRON
geusb-8353	133	2	presents	present	VERB
geusb-8353	133	3	an	an	DET
geusb-8353	133	4	improvement	improvement	NOUN
geusb-8353	133	5	over	over	ADP
geusb-8353	133	6	,	,	PUNCT
geusb-8353	133	7	for	for	ADP
geusb-8353	133	8	example	example	NOUN
geusb-8353	133	9	,	,	PUNCT
geusb-8353	133	10	a	a	DET
geusb-8353	133	11	traditional	traditional	ADJ
geusb-8353	133	12	semivariogram	semivariogram	NOUN
geusb-8353	133	13	analysis	analysis	NOUN
geusb-8353	133	14	,	,	PUNCT
geusb-8353	133	15	the	the	DET
geusb-8353	133	16	performance	performance	NOUN
geusb-8353	133	17	of	of	ADP
geusb-8353	133	18	which	which	PRON
geusb-8353	133	19	comes	come	VERB
geusb-8353	133	20	with	with	ADP
geusb-8353	133	21	a	a	DET
geusb-8353	133	22	tradeoff	tradeoff	NOUN
geusb-8353	133	23	between	between	ADP
geusb-8353	133	24	the	the	DET
geusb-8353	133	25	ability	ability	NOUN
geusb-8353	133	26	to	to	PART
geusb-8353	133	27	accurately	accurately	ADV
geusb-8353	133	28	predict	predict	VERB
geusb-8353	133	29	shorter	short	ADJ
geusb-8353	133	30	versus	versus	ADP
geusb-8353	133	31	longer	long	ADJ
geusb-8353	133	32	ranges	range	NOUN
geusb-8353	133	33	.	.	PUNCT
geusb-8353	134	1	this	this	DET
geusb-8353	134	2	trade	trade	NOUN
geusb-8353	134	3	-	-	PUNCT
geusb-8353	134	4	off	off	NOUN
geusb-8353	134	5	occurs	occur	VERB
geusb-8353	134	6	due	due	ADP
geusb-8353	134	7	to	to	ADP
geusb-8353	134	8	the	the	DET
geusb-8353	134	9	constraints	constraint	NOUN
geusb-8353	134	10	imposed	impose	VERB
geusb-8353	134	11	by	by	ADP
geusb-8353	134	12	the	the	DET
geusb-8353	134	13	weights	weight	NOUN
geusb-8353	134	14	on	on	ADP
geusb-8353	134	15	the	the	DET
geusb-8353	134	16	experimental	experimental	ADJ
geusb-8353	134	17	variogram	variogram	NOUN
geusb-8353	134	18	at	at	ADP
geusb-8353	134	19	different	different	ADJ
geusb-8353	134	20	range	range	NOUN
geusb-8353	134	21	intervals	interval	NOUN
geusb-8353	134	22	.	.	PUNCT
geusb-8353	135	1	the	the	DET
geusb-8353	135	2	nn	nn	PROPN
geusb-8353	135	3	architecture	architecture	NOUN
geusb-8353	135	4	used	use	VERB
geusb-8353	135	5	from	from	ADP
geusb-8353	135	6	squeezenet	squeezenet	NOUN
geusb-8353	135	7	(	(	PUNCT
geusb-8353	135	8	iandola	iandola	PROPN
geusb-8353	135	9	et	et	PROPN
geusb-8353	135	10	  	  	SPACE
geusb-8353	135	11	al	al	PROPN
geusb-8353	135	12	.	.	PROPN
geusb-8353	135	13	2016	2016	NUM
geusb-8353	135	14	)	)	PUNCT
geusb-8353	135	15	is	be	AUX
geusb-8353	135	16	ideal	ideal	ADJ
geusb-8353	135	17	for	for	ADP
geusb-8353	135	18	obtaining	obtain	VERB
geusb-8353	135	19	good	good	ADJ
geusb-8353	135	20	predictions	prediction	NOUN
geusb-8353	135	21	by	by	ADP
geusb-8353	135	22	training	train	VERB
geusb-8353	135	23	the	the	DET
geusb-8353	135	24	model	model	NOUN
geusb-8353	135	25	using	use	VERB
geusb-8353	135	26	a	a	DET
geusb-8353	135	27	conventional	conventional	ADJ
geusb-8353	135	28	gradient	gradient	ADJ
geusb-8353	135	29	descent	descent	NOUN
geusb-8353	135	30	algorithm	algorithm	NOUN
geusb-8353	135	31	with	with	ADP
geusb-8353	135	32	a	a	DET
geusb-8353	135	33	sufficiently	sufficiently	ADV
geusb-8353	135	34	large	large	ADJ
geusb-8353	135	35	training	training	NOUN
geusb-8353	135	36	data	datum	NOUN
geusb-8353	135	37	set	set	VERB
geusb-8353	135	38	.	.	PUNCT
geusb-8353	136	1	we	we	PRON
geusb-8353	136	2	found	find	VERB
geusb-8353	136	3	that	that	SCONJ
geusb-8353	136	4	optimal	optimal	ADJ
geusb-8353	136	5	results	result	NOUN
geusb-8353	136	6	are	be	AUX
geusb-8353	136	7	obtained	obtain	VERB
geusb-8353	136	8	when	when	SCONJ
geusb-8353	136	9	training	train	VERB
geusb-8353	136	10	on	on	ADP
geusb-8353	136	11	at	at	ADV
geusb-8353	136	12	least	least	ADV
geusb-8353	136	13	100	100	NUM
geusb-8353	136	14	  	  	SPACE
geusb-8353	136	15	000	000	NUM
geusb-8353	136	16	independent	independent	ADJ
geusb-8353	136	17	realisations	realisation	NOUN
geusb-8353	136	18	,	,	PUNCT
geusb-8353	136	19	with	with	ADP
geusb-8353	136	20	performance	performance	NOUN
geusb-8353	136	21	severely	severely	ADV
geusb-8353	136	22	decreasing	decrease	VERB
geusb-8353	136	23	when	when	SCONJ
geusb-8353	136	24	training	train	VERB
geusb-8353	136	25	on	on	ADP
geusb-8353	136	26	less	less	ADJ
geusb-8353	136	27	than	than	ADP
geusb-8353	136	28	10	10	NUM
geusb-8353	136	29	 	 	SPACE
geusb-8353	136	30	000	000	NUM
geusb-8353	136	31	realisations	realisation	NOUN
geusb-8353	136	32	.	.	PUNCT
geusb-8353	137	1	the	the	DET
geusb-8353	137	2	presented	present	VERB
geusb-8353	137	3	approach	approach	NOUN
geusb-8353	137	4	also	also	ADV
geusb-8353	137	5	has	have	VERB
geusb-8353	137	6	the	the	DET
geusb-8353	137	7	major	major	ADJ
geusb-8353	137	8	advantage	advantage	NOUN
geusb-8353	137	9	of	of	ADP
geusb-8353	137	10	having	have	VERB
geusb-8353	137	11	limitless	limitless	ADJ
geusb-8353	137	12	training	training	NOUN
geusb-8353	137	13	data	datum	NOUN
geusb-8353	137	14	available	available	ADJ
geusb-8353	137	15	,	,	PUNCT
geusb-8353	137	16	although	although	SCONJ
geusb-8353	137	17	a	a	DET
geusb-8353	137	18	reasonable	reasonable	ADJ
geusb-8353	137	19	amount	amount	NOUN
geusb-8353	137	20	of	of	ADP
geusb-8353	137	21	training	training	NOUN
geusb-8353	137	22	data	datum	NOUN
geusb-8353	137	23	should	should	AUX
geusb-8353	137	24	be	be	AUX
geusb-8353	137	25	chosen	choose	VERB
geusb-8353	137	26	for	for	ADP
geusb-8353	137	27	computational	computational	ADJ
geusb-8353	137	28	feasibility	feasibility	NOUN
geusb-8353	137	29	.	.	PUNCT
geusb-8353	138	1	the	the	DET
geusb-8353	138	2	network	network	NOUN
geusb-8353	138	3	can	can	AUX
geusb-8353	138	4	also	also	ADV
geusb-8353	138	5	be	be	AUX
geusb-8353	138	6	trained	train	VERB
geusb-8353	138	7	to	to	ADP
geusb-8353	138	8	a	a	DET
geusb-8353	138	9	larger	large	ADJ
geusb-8353	138	10	grid	grid	NOUN
geusb-8353	138	11	than	than	ADP
geusb-8353	138	12	the	the	DET
geusb-8353	138	13	31	31	NUM
geusb-8353	138	14	×	×	NOUN
geusb-8353	138	15	31	31	NUM
geusb-8353	138	16	grid	grid	NOUN
geusb-8353	138	17	used	use	VERB
geusb-8353	138	18	here	here	ADV
geusb-8353	138	19	,	,	PUNCT
geusb-8353	138	20	giving	give	VERB
geusb-8353	138	21	a	a	DET
geusb-8353	138	22	lot	lot	NOUN
geusb-8353	138	23	of	of	ADP
geusb-8353	138	24	flexibility	flexibility	NOUN
geusb-8353	138	25	for	for	ADP
geusb-8353	138	26	the	the	DET
geusb-8353	138	27	specific	specific	ADJ
geusb-8353	138	28	model	model	NOUN
geusb-8353	138	29	for	for	ADP
geusb-8353	138	30	which	which	PRON
geusb-8353	138	31	inference	inference	NOUN
geusb-8353	138	32	is	be	AUX
geusb-8353	138	33	needed	need	VERB
geusb-8353	138	34	.	.	PUNCT
geusb-8353	139	1	the	the	DET
geusb-8353	139	2	31	31	NUM
geusb-8353	139	3	×	×	NOUN
geusb-8353	139	4	31	31	NUM
geusb-8353	139	5	grid	grid	NOUN
geusb-8353	139	6	posed	pose	VERB
geusb-8353	139	7	some	some	DET
geusb-8353	139	8	issues	issue	NOUN
geusb-8353	139	9	for	for	ADP
geusb-8353	139	10	estimating	estimate	VERB
geusb-8353	139	11	ranges	range	NOUN
geusb-8353	139	12	over	over	ADP
geusb-8353	139	13	a	a	DET
geusb-8353	139	14	certain	certain	ADJ
geusb-8353	139	15	length	length	NOUN
geusb-8353	139	16	.	.	PUNCT
geusb-8353	140	1	this	this	DET
geusb-8353	140	2	limitation	limitation	NOUN
geusb-8353	140	3	to	to	ADP
geusb-8353	140	4	the	the	DET
geusb-8353	140	5	method	method	NOUN
geusb-8353	140	6	can	can	AUX
geusb-8353	140	7	be	be	AUX
geusb-8353	140	8	remedied	remedie	VERB
geusb-8353	140	9	by	by	ADP
geusb-8353	140	10	increasing	increase	VERB
geusb-8353	140	11	the	the	DET
geusb-8353	140	12	grid	grid	NOUN
geusb-8353	140	13	size	size	NOUN
geusb-8353	140	14	,	,	PUNCT
geusb-8353	140	15	but	but	CCONJ
geusb-8353	140	16	at	at	ADP
geusb-8353	140	17	the	the	DET
geusb-8353	140	18	cost	cost	NOUN
geusb-8353	140	19	of	of	ADP
geusb-8353	140	20	increasing	increase	VERB
geusb-8353	140	21	computation	computation	NOUN
geusb-8353	140	22	time	time	NOUN
geusb-8353	140	23	during	during	ADP
geusb-8353	140	24	training	training	NOUN
geusb-8353	140	25	of	of	ADP
geusb-8353	140	26	the	the	DET
geusb-8353	140	27	cnn	cnn	PROPN
geusb-8353	140	28	.	.	PUNCT
geusb-8353	141	1	to	to	PART
geusb-8353	141	2	determine	determine	VERB
geusb-8353	141	3	the	the	DET
geusb-8353	141	4	right	right	ADJ
geusb-8353	141	5	size	size	NOUN
geusb-8353	141	6	of	of	ADP
geusb-8353	141	7	the	the	DET
geusb-8353	141	8	grid	grid	NOUN
geusb-8353	141	9	in	in	ADP
geusb-8353	141	10	a	a	DET
geusb-8353	141	11	practical	practical	ADJ
geusb-8353	141	12	case	case	NOUN
geusb-8353	141	13	,	,	PUNCT
geusb-8353	141	14	we	we	PRON
geusb-8353	141	15	suggest	suggest	VERB
geusb-8353	141	16	training	train	VERB
geusb-8353	141	17	two	two	NUM
geusb-8353	141	18	preliminary	preliminary	ADJ
geusb-8353	141	19	networks	network	NOUN
geusb-8353	141	20	with	with	ADP
geusb-8353	141	21	a	a	DET
geusb-8353	141	22	small	small	ADJ
geusb-8353	141	23	grid	grid	NOUN
geusb-8353	141	24	and	and	CCONJ
geusb-8353	141	25	a	a	DET
geusb-8353	141	26	slightly	slightly	ADV
geusb-8353	141	27	larger	large	ADJ
geusb-8353	141	28	grid	grid	NOUN
geusb-8353	141	29	.	.	PUNCT
geusb-8353	142	1	if	if	SCONJ
geusb-8353	142	2	predictions	prediction	NOUN
geusb-8353	142	3	with	with	ADP
geusb-8353	142	4	these	these	DET
geusb-8353	142	5	two	two	NUM
geusb-8353	142	6	networks	network	NOUN
geusb-8353	142	7	deviate	deviate	VERB
geusb-8353	142	8	significantly	significantly	ADV
geusb-8353	142	9	when	when	SCONJ
geusb-8353	142	10	applied	apply	VERB
geusb-8353	142	11	on	on	ADP
geusb-8353	142	12	real	real	ADJ
geusb-8353	142	13	data	datum	NOUN
geusb-8353	142	14	,	,	PUNCT
geusb-8353	142	15	the	the	DET
geusb-8353	142	16	grid	grid	NOUN
geusb-8353	142	17	size	size	NOUN
geusb-8353	142	18	must	must	AUX
geusb-8353	142	19	be	be	AUX
geusb-8353	142	20	increased	increase	VERB
geusb-8353	142	21	to	to	PART
geusb-8353	142	22	accommodate	accommodate	VERB
geusb-8353	142	23	all	all	DET
geusb-8353	142	24	possible	possible	ADJ
geusb-8353	142	25	ranges	range	NOUN
geusb-8353	142	26	.	.	PUNCT
geusb-8353	143	1	the	the	DET
geusb-8353	143	2	process	process	NOUN
geusb-8353	143	3	can	can	AUX
geusb-8353	143	4	be	be	AUX
geusb-8353	143	5	repeated	repeat	VERB
geusb-8353	143	6	iteratively	iteratively	ADV
geusb-8353	143	7	until	until	SCONJ
geusb-8353	143	8	the	the	DET
geusb-8353	143	9	same	same	ADJ
geusb-8353	143	10	range	range	NOUN
geusb-8353	143	11	interval	interval	NOUN
geusb-8353	143	12	is	be	AUX
geusb-8353	143	13	predicted	predict	VERB
geusb-8353	143	14	for	for	ADP
geusb-8353	143	15	both	both	DET
geusb-8353	143	16	networks	network	NOUN
geusb-8353	143	17	,	,	PUNCT
geusb-8353	143	18	indicating	indicate	VERB
geusb-8353	143	19	a	a	DET
geusb-8353	143	20	reasonable	reasonable	ADJ
geusb-8353	143	21	minimum	minimum	ADJ
geusb-8353	143	22	grid	grid	NOUN
geusb-8353	143	23	size	size	NOUN
geusb-8353	143	24	.	.	PUNCT
geusb-8353	144	1	using	use	VERB
geusb-8353	144	2	this	this	DET
geusb-8353	144	3	strategy	strategy	NOUN
geusb-8353	144	4	for	for	ADP
geusb-8353	144	5	the	the	DET
geusb-8353	144	6	dk	dk	PROPN
geusb-8353	144	7	-	-	NOUN
geusb-8353	144	8	model	model	NOUN
geusb-8353	144	9	,	,	PUNCT
geusb-8353	144	10	a	a	DET
geusb-8353	144	11	31	31	NUM
geusb-8353	144	12	×	×	NOUN
geusb-8353	144	13	31	31	NUM
geusb-8353	144	14	grid	grid	NOUN
geusb-8353	144	15	was	be	AUX
geusb-8353	144	16	deemed	deem	VERB
geusb-8353	144	17	suitable	suitable	ADJ
geusb-8353	144	18	as	as	SCONJ
geusb-8353	144	19	showcased	showcase	VERB
geusb-8353	144	20	.	.	PUNCT
geusb-8353	145	1	our	our	PRON
geusb-8353	145	2	results	result	NOUN
geusb-8353	145	3	in	in	ADP
geusb-8353	145	4	the	the	DET
geusb-8353	145	5	synthetic	synthetic	ADJ
geusb-8353	145	6	case	case	NOUN
geusb-8353	145	7	suggest	suggest	VERB
geusb-8353	145	8	that	that	SCONJ
geusb-8353	145	9	the	the	DET
geusb-8353	145	10	deployed	deploy	VERB
geusb-8353	145	11	cnn	cnn	PROPN
geusb-8353	145	12	has	have	VERB
geusb-8353	145	13	the	the	DET
geusb-8353	145	14	same	same	ADJ
geusb-8353	145	15	accuracy	accuracy	NOUN
geusb-8353	145	16	as	as	ADP
geusb-8353	145	17	a	a	DET
geusb-8353	145	18	traditional	traditional	ADJ
geusb-8353	145	19	automatic	automatic	ADJ
geusb-8353	145	20	semivariogram	semivariogram	NOUN
geusb-8353	145	21	fitting	fitting	ADJ
geusb-8353	145	22	,	,	PUNCT
geusb-8353	145	23	but	but	CCONJ
geusb-8353	145	24	with	with	ADP
geusb-8353	145	25	a	a	DET
geusb-8353	145	26	substantial	substantial	ADJ
geusb-8353	145	27	improvement	improvement	NOUN
geusb-8353	145	28	in	in	ADP
geusb-8353	145	29	speed	speed	NOUN
geusb-8353	145	30	,	,	PUNCT
geusb-8353	145	31	which	which	PRON
geusb-8353	145	32	now	now	ADV
geusb-8353	145	33	makes	make	VERB
geusb-8353	145	34	it	it	PRON
geusb-8353	145	35	feasible	feasible	ADJ
geusb-8353	145	36	to	to	PART
geusb-8353	145	37	analyse	analyse	VERB
geusb-8353	145	38	large	large	ADJ
geusb-8353	145	39	grids	grid	NOUN
geusb-8353	145	40	within	within	ADP
geusb-8353	145	41	a	a	DET
geusb-8353	145	42	reasonable	reasonable	ADJ
geusb-8353	145	43	amount	amount	NOUN
geusb-8353	145	44	of	of	ADP
geusb-8353	145	45	fig	fig	NOUN
geusb-8353	145	46	.	.	PUNCT
geusb-8353	146	1	4	4	NUM
geusb-8353	146	2	 	 	SPACE
geusb-8353	146	3	method	method	NOUN
geusb-8353	146	4	results	result	NOUN
geusb-8353	146	5	of	of	ADP
geusb-8353	146	6	a	a	DET
geusb-8353	146	7	layer	layer	NOUN
geusb-8353	146	8	covering	cover	VERB
geusb-8353	146	9	the	the	DET
geusb-8353	146	10	danish	danish	ADJ
geusb-8353	146	11	island	island	NOUN
geusb-8353	146	12	of	of	ADP
geusb-8353	146	13	fyn	fyn	PROPN
geusb-8353	146	14	.	.	PUNCT
geusb-8353	147	1	(	(	PUNCT
geusb-8353	147	2	a	a	X
geusb-8353	147	3	)	)	PUNCT
geusb-8353	147	4	predicted	predict	VERB
geusb-8353	147	5	ranges	range	NOUN
geusb-8353	147	6	.	.	PUNCT
geusb-8353	148	1	(	(	PUNCT
geusb-8353	148	2	b	b	X
geusb-8353	148	3	)	)	PUNCT
geusb-8353	148	4	ranges	range	NOUN
geusb-8353	148	5	clustered	cluster	VERB
geusb-8353	148	6	into	into	ADP
geusb-8353	148	7	regions	region	NOUN
geusb-8353	148	8	.	.	PUNCT
geusb-8353	149	1	(	(	PUNCT
geusb-8353	149	2	c	c	X
geusb-8353	149	3	)	)	PUNCT
geusb-8353	149	4	layer	layer	NOUN
geusb-8353	149	5	elevation	elevation	NOUN
geusb-8353	149	6	across	across	ADP
geusb-8353	149	7	the	the	DET
geusb-8353	149	8	black	black	ADJ
geusb-8353	149	9	profile	profile	NOUN
geusb-8353	149	10	shown	show	VERB
geusb-8353	149	11	in	in	ADP
geusb-8353	149	12	(	(	PUNCT
geusb-8353	149	13	a	a	NOUN
geusb-8353	149	14	)	)	PUNCT
geusb-8353	149	15	and	and	CCONJ
geusb-8353	149	16	(	(	PUNCT
geusb-8353	149	17	b	b	NOUN
geusb-8353	149	18	)	)	PUNCT
geusb-8353	149	19	and	and	CCONJ
geusb-8353	149	20	the	the	DET
geusb-8353	149	21	predicted	predict	VERB
geusb-8353	149	22	ranges	range	NOUN
geusb-8353	149	23	(	(	PUNCT
geusb-8353	149	24	green	green	ADJ
geusb-8353	149	25	/	/	SYM
geusb-8353	149	26	white	white	ADJ
geusb-8353	149	27	line	line	NOUN
geusb-8353	149	28	)	)	PUNCT
geusb-8353	149	29	across	across	ADP
geusb-8353	149	30	the	the	DET
geusb-8353	149	31	same	same	ADJ
geusb-8353	149	32	profile	profile	NOUN
geusb-8353	149	33	.	.	PUNCT
geusb-8353	150	1	the	the	DET
geusb-8353	150	2	colour	colour	ADJ
geusb-8353	150	3	scale	scale	NOUN
geusb-8353	150	4	in	in	ADP
geusb-8353	150	5	(	(	PUNCT
geusb-8353	150	6	c	c	NOUN
geusb-8353	150	7	)	)	PUNCT
geusb-8353	150	8	matches	match	VERB
geusb-8353	150	9	the	the	DET
geusb-8353	150	10	colour	colour	ADJ
geusb-8353	150	11	scale	scale	NOUN
geusb-8353	150	12	in	in	ADP
geusb-8353	150	13	(	(	PUNCT
geusb-8353	150	14	b	b	NOUN
geusb-8353	150	15	)	)	PUNCT
geusb-8353	150	16	,	,	PUNCT
geusb-8353	150	17	showing	show	VERB
geusb-8353	150	18	where	where	SCONJ
geusb-8353	150	19	individual	individual	ADJ
geusb-8353	150	20	clusters	cluster	NOUN
geusb-8353	150	21	are	be	AUX
geusb-8353	150	22	located	locate	VERB
geusb-8353	150	23	.	.	PUNCT
geusb-8353	151	1	(	(	PUNCT
geusb-8353	151	2	a	a	X
geusb-8353	151	3	)	)	PUNCT
geusb-8353	151	4	range	range	NOUN
geusb-8353	151	5	map	map	VERB
geusb-8353	151	6	5.6	5.6	NUM
geusb-8353	151	7	5.8	5.8	NUM
geusb-8353	151	8	6	6	NUM
geusb-8353	151	9	6.2	6.2	NUM
geusb-8353	151	10	utm	utm	NOUN
geusb-8353	151	11	x	x	PUNCT
geusb-8353	152	1	[	[	X
geusb-8353	152	2	m	m	X
geusb-8353	152	3	]	]	X
geusb-8353	152	4	6.08	6.08	NUM
geusb-8353	152	5	6.1	6.1	NUM
geusb-8353	152	6	6.12	6.12	NUM
geusb-8353	152	7	6.14	6.14	NUM
geusb-8353	152	8	6.16	6.16	NUM
geusb-8353	152	9	u	u	NOUN
geusb-8353	152	10	t	t	NOUN
geusb-8353	152	11	m	m	VERB
geusb-8353	152	12	y	y	PROPN
geusb-8353	152	13	[	[	X
geusb-8353	152	14	m	m	X
geusb-8353	152	15	]	]	X
geusb-8353	152	16	×106	×106	NOUN
geusb-8353	152	17	0	0	NUM
geusb-8353	152	18	1000	1000	NUM
geusb-8353	152	19	2000	2000	NUM
geusb-8353	152	20	3000	3000	NUM
geusb-8353	152	21	range	range	NOUN
geusb-8353	153	1	[	[	X
geusb-8353	153	2	m	m	X
geusb-8353	153	3	]	]	X
geusb-8353	153	4	(	(	PUNCT
geusb-8353	153	5	b	b	X
geusb-8353	153	6	)	)	PUNCT
geusb-8353	153	7	cluster	cluster	NOUN
geusb-8353	153	8	map	map	NOUN
geusb-8353	153	9	5.6	5.6	NUM
geusb-8353	153	10	5.8	5.8	NUM
geusb-8353	153	11	6	6	NUM
geusb-8353	153	12	6.2	6.2	NUM
geusb-8353	153	13	utm	utm	NOUN
geusb-8353	153	14	x	x	PUNCT
geusb-8353	154	1	[	[	X
geusb-8353	154	2	m	m	X
geusb-8353	154	3	]	]	X
geusb-8353	154	4	1	1	NUM
geusb-8353	154	5	2	2	NUM
geusb-8353	154	6	3	3	NUM
geusb-8353	154	7	4	4	NUM
geusb-8353	154	8	5	5	NUM
geusb-8353	154	9	6	6	NUM
geusb-8353	154	10	7	7	NUM
geusb-8353	154	11	8	8	NUM
geusb-8353	154	12	9	9	NUM
geusb-8353	154	13	10	10	NUM
geusb-8353	154	14	11	11	NUM
geusb-8353	154	15	12	12	NUM
geusb-8353	154	16	13	13	NUM
geusb-8353	154	17	14	14	NUM
geusb-8353	154	18	15	15	NUM
geusb-8353	154	19	16	16	NUM
geusb-8353	154	20	17	17	NUM
geusb-8353	154	21	18	18	NUM
geusb-8353	154	22	cluster	cluster	NOUN
geusb-8353	154	23	index	index	NOUN
geusb-8353	154	24	(	(	PUNCT
geusb-8353	154	25	c	c	NOUN
geusb-8353	154	26	)	)	PUNCT
geusb-8353	154	27	profile	profile	NOUN
geusb-8353	154	28	view	view	VERB
geusb-8353	154	29	5.65	5.65	NUM
geusb-8353	154	30	5.7	5.7	NUM
geusb-8353	154	31	5.75	5.75	NUM
geusb-8353	154	32	5.8	5.8	NUM
geusb-8353	154	33	5.85	5.85	NUM
geusb-8353	154	34	5.9	5.9	NUM
geusb-8353	154	35	5.95	5.95	NUM
geusb-8353	154	36	6	6	NUM
geusb-8353	154	37	6.05	6.05	NUM
geusb-8353	154	38	6.1	6.1	NUM
geusb-8353	154	39	utm	utm	PROPN
geusb-8353	154	40	x	x	PUNCT
geusb-8353	155	1	[	[	X
geusb-8353	155	2	m	m	X
geusb-8353	155	3	]	]	X
geusb-8353	155	4	0	0	NUM
geusb-8353	155	5	20	20	NUM
geusb-8353	155	6	40	40	NUM
geusb-8353	155	7	60	60	NUM
geusb-8353	155	8	80	80	NUM
geusb-8353	155	9	100	100	NUM
geusb-8353	155	10	e	e	X
geusb-8353	155	11	le	le	X
geusb-8353	155	12	va	va	PROPN
geusb-8353	155	13	tio	tio	PROPN
geusb-8353	155	14	n	n	PROPN
geusb-8353	156	1	[	[	X
geusb-8353	156	2	m	m	X
geusb-8353	156	3	]	]	X
geusb-8353	156	4	500	500	NUM
geusb-8353	156	5	1000	1000	NUM
geusb-8353	156	6	1500	1500	NUM
geusb-8353	156	7	2000	2000	NUM
geusb-8353	156	8	2500	2500	NUM
geusb-8353	156	9	r	r	NOUN
geusb-8353	156	10	an	an	DET
geusb-8353	156	11	ge	ge	PROPN
geusb-8353	157	1	[	[	X
geusb-8353	157	2	m	m	X
geusb-8353	157	3	]	]	X
geusb-8353	157	4	×105	×105	ADJ
geusb-8353	157	5	×105	×105	PROPN
geusb-8353	157	6	×105	×105	PROPN
geusb-8353	157	7	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	157	8	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	157	9	falk	falk	NOUN
geusb-8353	157	10	&	&	CCONJ
geusb-8353	157	11	madsen	madsen	PROPN
geusb-8353	157	12	2023	2023	NUM
geusb-8353	157	13	:	:	PUNCT
geusb-8353	157	14	geus	geus	NOUN
geusb-8353	157	15	bulletin	bulletin	NOUN
geusb-8353	157	16	53	53	NUM
geusb-8353	157	17	.	.	PUNCT
geusb-8353	157	18	8353	8353	NUM
geusb-8353	157	19	.	.	PUNCT
geusb-8353	158	1	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	158	2	7	7	NUM
geusb-8353	158	3	of	of	ADP
geusb-8353	158	4	7	7	NUM
geusb-8353	158	5	www.geusbul	www.geusbul	NOUN
geusb-8353	158	6	let	let	VERB
geusb-8353	158	7	in.org	in.org	ADJ
geusb-8353	158	8	computation	computation	NOUN
geusb-8353	158	9	time	time	NOUN
geusb-8353	158	10	as	as	SCONJ
geusb-8353	158	11	showcased	showcase	VERB
geusb-8353	158	12	in	in	ADP
geusb-8353	158	13	the	the	DET
geusb-8353	158	14	final	final	ADJ
geusb-8353	158	15	validation	validation	NOUN
geusb-8353	158	16	example	example	NOUN
geusb-8353	158	17	to	to	ADP
geusb-8353	158	18	account	account	VERB
geusb-8353	158	19	for	for	ADP
geusb-8353	158	20	non	non	ADJ
geusb-8353	158	21	-	-	NOUN
geusb-8353	158	22	stationarity	stationarity	NOUN
geusb-8353	158	23	.	.	PUNCT
geusb-8353	159	1	the	the	DET
geusb-8353	159	2	subsequent	subsequent	ADJ
geusb-8353	159	3	clustering	clustering	NOUN
geusb-8353	159	4	also	also	ADV
geusb-8353	159	5	makes	make	VERB
geusb-8353	159	6	it	it	PRON
geusb-8353	159	7	possible	possible	ADJ
geusb-8353	159	8	to	to	PART
geusb-8353	159	9	define	define	VERB
geusb-8353	159	10	regions	region	NOUN
geusb-8353	159	11	with	with	ADP
geusb-8353	159	12	comparable	comparable	ADJ
geusb-8353	159	13	statistics	statistic	NOUN
geusb-8353	159	14	in	in	ADP
geusb-8353	159	15	the	the	DET
geusb-8353	159	16	stratigraphical	stratigraphical	ADJ
geusb-8353	159	17	model	model	NOUN
geusb-8353	159	18	.	.	PUNCT
geusb-8353	160	1	for	for	ADP
geusb-8353	160	2	further	further	ADJ
geusb-8353	160	3	application	application	NOUN
geusb-8353	160	4	of	of	ADP
geusb-8353	160	5	the	the	DET
geusb-8353	160	6	estimated	estimate	VERB
geusb-8353	160	7	statistical	statistical	ADJ
geusb-8353	160	8	models	model	NOUN
geusb-8353	160	9	,	,	PUNCT
geusb-8353	160	10	one	one	PRON
geusb-8353	160	11	could	could	AUX
geusb-8353	160	12	infer	infer	VERB
geusb-8353	160	13	the	the	DET
geusb-8353	160	14	local	local	ADJ
geusb-8353	160	15	statistics	statistic	NOUN
geusb-8353	160	16	from	from	ADP
geusb-8353	160	17	the	the	DET
geusb-8353	160	18	sill	sill	ADJ
geusb-8353	160	19	and	and	CCONJ
geusb-8353	160	20	range	range	NOUN
geusb-8353	160	21	estimates	estimate	NOUN
geusb-8353	160	22	within	within	ADP
geusb-8353	160	23	each	each	DET
geusb-8353	160	24	cluster	cluster	NOUN
geusb-8353	160	25	and	and	CCONJ
geusb-8353	160	26	use	use	VERB
geusb-8353	160	27	these	these	PRON
geusb-8353	160	28	for	for	ADP
geusb-8353	160	29	,	,	PUNCT
geusb-8353	160	30	for	for	ADP
geusb-8353	160	31	example	example	NOUN
geusb-8353	160	32	,	,	PUNCT
geusb-8353	160	33	localised	localised	ADJ
geusb-8353	160	34	geostatistical	geostatistical	ADJ
geusb-8353	160	35	estimation	estimation	NOUN
geusb-8353	160	36	(	(	PUNCT
geusb-8353	160	37	kriging	kriging	NOUN
geusb-8353	160	38	)	)	PUNCT
geusb-8353	160	39	or	or	CCONJ
geusb-8353	160	40	simulation	simulation	NOUN
geusb-8353	160	41	of	of	ADP
geusb-8353	160	42	each	each	DET
geusb-8353	160	43	cluster	cluster	NOUN
geusb-8353	160	44	instead	instead	ADV
geusb-8353	160	45	of	of	ADP
geusb-8353	160	46	using	use	VERB
geusb-8353	160	47	a	a	DET
geusb-8353	160	48	stationary	stationary	ADJ
geusb-8353	160	49	model	model	NOUN
geusb-8353	160	50	as	as	SCONJ
geusb-8353	160	51	done	do	VERB
geusb-8353	160	52	in	in	ADP
geusb-8353	160	53	madsen	madsen	PROPN
geusb-8353	160	54	et	et	PROPN
geusb-8353	160	55	al	al	PROPN
geusb-8353	160	56	.	.	PROPN
geusb-8353	161	1	(	(	PUNCT
geusb-8353	161	2	2022	2022	NUM
geusb-8353	161	3	)	)	PUNCT
geusb-8353	161	4	for	for	ADP
geusb-8353	161	5	a	a	DET
geusb-8353	161	6	hydrostratigraphic	hydrostratigraphic	ADJ
geusb-8353	161	7	model	model	NOUN
geusb-8353	161	8	.	.	PUNCT
geusb-8353	162	1	acknowledgements	acknowledgement	NOUN
geusb-8353	162	2	the	the	DET
geusb-8353	162	3	authors	author	NOUN
geusb-8353	162	4	would	would	AUX
geusb-8353	162	5	like	like	VERB
geusb-8353	162	6	to	to	PART
geusb-8353	162	7	acknowledge	acknowledge	VERB
geusb-8353	162	8	the	the	DET
geusb-8353	162	9	geological	geological	ADJ
geusb-8353	162	10	survey	survey	NOUN
geusb-8353	162	11	of	of	ADP
geusb-8353	162	12	denmark	denmark	NOUN
geusb-8353	162	13	and	and	CCONJ
geusb-8353	162	14	greenland	greenland	PROPN
geusb-8353	162	15	(	(	PUNCT
geusb-8353	162	16	geus	geus	NOUN
geusb-8353	162	17	)	)	PUNCT
geusb-8353	162	18	for	for	ADP
geusb-8353	162	19	providing	provide	VERB
geusb-8353	162	20	funding	funding	NOUN
geusb-8353	162	21	for	for	ADP
geusb-8353	162	22	writing	write	VERB
geusb-8353	162	23	the	the	DET
geusb-8353	162	24	manuscript	manuscript	NOUN
geusb-8353	162	25	as	as	SCONJ
geusb-8353	162	26	the	the	DET
geusb-8353	162	27	method	method	NOUN
geusb-8353	162	28	development	development	NOUN
geusb-8353	162	29	was	be	AUX
geusb-8353	162	30	carried	carry	VERB
geusb-8353	162	31	out	out	ADP
geusb-8353	162	32	in	in	ADP
geusb-8353	162	33	a	a	DET
geusb-8353	162	34	consultancy	consultancy	NOUN
geusb-8353	162	35	project	project	NOUN
geusb-8353	162	36	without	without	ADP
geusb-8353	162	37	funding	fund	VERB
geusb-8353	162	38	for	for	ADP
geusb-8353	162	39	scientific	scientific	ADJ
geusb-8353	162	40	publication	publication	NOUN
geusb-8353	162	41	.	.	PUNCT
geusb-8353	163	1	additional	additional	ADJ
geusb-8353	163	2	information	information	NOUN
geusb-8353	163	3	funding	funding	NOUN
geusb-8353	163	4	statement	statement	NOUN
geusb-8353	163	5	the	the	DET
geusb-8353	163	6	method	method	NOUN
geusb-8353	163	7	was	be	AUX
geusb-8353	163	8	developed	develop	VERB
geusb-8353	163	9	during	during	ADP
geusb-8353	163	10	consultancy	consultancy	NOUN
geusb-8353	163	11	work	work	NOUN
geusb-8353	163	12	for	for	ADP
geusb-8353	163	13	the	the	DET
geusb-8353	163	14	danish	danish	PROPN
geusb-8353	163	15	epa	epa	PROPN
geusb-8353	163	16	,	,	PUNCT
geusb-8353	163	17	whilst	whilst	SCONJ
geusb-8353	163	18	the	the	DET
geusb-8353	163	19	hours	hour	NOUN
geusb-8353	163	20	for	for	ADP
geusb-8353	163	21	turning	turn	VERB
geusb-8353	163	22	the	the	DET
geusb-8353	163	23	results	result	NOUN
geusb-8353	163	24	into	into	ADP
geusb-8353	163	25	a	a	DET
geusb-8353	163	26	scientific	scientific	ADJ
geusb-8353	163	27	contribution	contribution	NOUN
geusb-8353	163	28	and	and	CCONJ
geusb-8353	163	29	writing	write	VERB
geusb-8353	163	30	the	the	DET
geusb-8353	163	31	manuscript	manuscript	NOUN
geusb-8353	163	32	were	be	AUX
geusb-8353	163	33	provided	provide	VERB
geusb-8353	163	34	by	by	ADP
geusb-8353	163	35	the	the	DET
geusb-8353	163	36	geological	geological	ADJ
geusb-8353	163	37	survey	survey	NOUN
geusb-8353	163	38	of	of	ADP
geusb-8353	163	39	greenland	greenland	PROPN
geusb-8353	163	40	and	and	CCONJ
geusb-8353	163	41	denmark	denmark	PROPN
geusb-8353	163	42	(	(	PUNCT
geusb-8353	163	43	geus	geus	NOUN
geusb-8353	163	44	)	)	PUNCT
geusb-8353	163	45	.	.	PUNCT
geusb-8353	164	1	author	author	NOUN
geusb-8353	164	2	contributions	contribution	NOUN
geusb-8353	164	3	faf	faf	VERB
geusb-8353	164	4	:	:	PUNCT
geusb-8353	164	5	methodology	methodology	NOUN
geusb-8353	164	6	,	,	PUNCT
geusb-8353	164	7	software	software	NOUN
geusb-8353	164	8	,	,	PUNCT
geusb-8353	164	9	investigation	investigation	NOUN
geusb-8353	164	10	,	,	PUNCT
geusb-8353	164	11	writing	write	VERB
geusb-8353	164	12	–	–	PUNCT
geusb-8353	164	13	original	original	ADJ
geusb-8353	164	14	draft	draft	NOUN
geusb-8353	164	15	preparation	preparation	NOUN
geusb-8353	164	16	rbm	rbm	PROPN
geusb-8353	164	17	:	:	PUNCT
geusb-8353	164	18	conceptualisation	conceptualisation	NOUN
geusb-8353	164	19	,	,	PUNCT
geusb-8353	164	20	methodology	methodology	NOUN
geusb-8353	164	21	,	,	PUNCT
geusb-8353	164	22	writing	write	VERB
geusb-8353	164	23	–	–	PUNCT
geusb-8353	164	24	reviewing	review	VERB
geusb-8353	164	25	and	and	CCONJ
geusb-8353	164	26	editing	edit	VERB
geusb-8353	164	27	competing	compete	VERB
geusb-8353	164	28	interests	interest	NOUN
geusb-8353	164	29	the	the	DET
geusb-8353	164	30	authors	author	NOUN
geusb-8353	164	31	declare	declare	VERB
geusb-8353	164	32	no	no	DET
geusb-8353	164	33	competing	compete	VERB
geusb-8353	164	34	interests	interest	NOUN
geusb-8353	164	35	.	.	PUNCT
geusb-8353	165	1	additional	additional	ADJ
geusb-8353	165	2	files	file	NOUN
geusb-8353	165	3	none	none	NOUN
geusb-8353	165	4	provided	provide	VERB
geusb-8353	165	5	references	reference	NOUN
geusb-8353	165	6	boisvert	boisvert	PROPN
geusb-8353	165	7	,	,	PUNCT
geusb-8353	165	8	j.b	j.b	PROPN
geusb-8353	165	9	.	.	PROPN
geusb-8353	165	10	&	&	CCONJ
geusb-8353	165	11	deutsch	deutsch	PROPN
geusb-8353	165	12	,	,	PUNCT
geusb-8353	165	13	c.v	c.v	PROPN
geusb-8353	165	14	.	.	PROPN
geusb-8353	165	15	2011	2011	NUM
geusb-8353	165	16	:	:	PUNCT
geusb-8353	165	17	programs	program	NOUN
geusb-8353	165	18	for	for	ADP
geusb-8353	165	19	kriging	krige	VERB
geusb-8353	165	20	and	and	CCONJ
geusb-8353	165	21	sequential	sequential	ADJ
geusb-8353	165	22	gaussian	gaussian	ADJ
geusb-8353	165	23	simulation	simulation	NOUN
geusb-8353	165	24	with	with	ADP
geusb-8353	165	25	locally	locally	ADV
geusb-8353	165	26	varying	vary	VERB
geusb-8353	165	27	anisotropy	anisotropy	NOUN
geusb-8353	165	28	using	use	VERB
geusb-8353	165	29	non	non	ADJ
geusb-8353	165	30	-	-	ADJ
geusb-8353	165	31	euclidean	euclidean	ADJ
geusb-8353	165	32	distances	distance	NOUN
geusb-8353	165	33	.	.	PUNCT
geusb-8353	166	1	computers	computer	NOUN
geusb-8353	166	2	&	&	CCONJ
geusb-8353	166	3	geosciences	geoscience	NOUN
geusb-8353	166	4	37(4	37(4	NUM
geusb-8353	166	5	)	)	PUNCT
geusb-8353	166	6	,	,	PUNCT
geusb-8353	166	7	495–510	495–510	NUM
geusb-8353	166	8	.	.	PUNCT
geusb-8353	167	1	https://doi	https://doi	X
geusb-8353	167	2	.	.	PUNCT
geusb-8353	168	1	org/10.1016	org/10.1016	PROPN
geusb-8353	168	2	/	/	SYM
geusb-8353	168	3	j.cageo.2010.03.021	j.cageo.2010.03.021	PROPN
geusb-8353	168	4	boisvert	boisvert	PROPN
geusb-8353	168	5	,	,	PUNCT
geusb-8353	168	6	j.b	j.b	PROPN
geusb-8353	168	7	.	.	PROPN
geusb-8353	168	8	,	,	PUNCT
geusb-8353	168	9	manchuk	manchuk	PROPN
geusb-8353	168	10	,	,	PUNCT
geusb-8353	168	11	j.	j.	PROPN
geusb-8353	168	12	&	&	CCONJ
geusb-8353	168	13	deutsch	deutsch	PROPN
geusb-8353	168	14	,	,	PUNCT
geusb-8353	168	15	c.v	c.v	PROPN
geusb-8353	168	16	.	.	PROPN
geusb-8353	168	17	2009	2009	NUM
geusb-8353	168	18	:	:	PUNCT
geusb-8353	169	1	kriging	krige	VERB
geusb-8353	169	2	in	in	ADP
geusb-8353	169	3	the	the	DET
geusb-8353	169	4	presence	presence	NOUN
geusb-8353	169	5	of	of	ADP
geusb-8353	169	6	locally	locally	ADV
geusb-8353	169	7	varying	vary	VERB
geusb-8353	169	8	anisotropy	anisotropy	NOUN
geusb-8353	169	9	using	use	VERB
geusb-8353	169	10	non	non	ADJ
geusb-8353	169	11	-	-	ADJ
geusb-8353	169	12	euclidean	euclidean	ADJ
geusb-8353	169	13	distances	distance	NOUN
geusb-8353	169	14	.	.	PUNCT
geusb-8353	170	1	mathematical	mathematical	ADJ
geusb-8353	170	2	geosciences	geoscience	NOUN
geusb-8353	170	3	41	41	NUM
geusb-8353	170	4	,	,	PUNCT
geusb-8353	170	5	585–601	585–601	NUM
geusb-8353	170	6	.	.	PUNCT
geusb-8353	171	1	https://doi.org/10.1007/s11004-009-9229-1	https://doi.org/10.1007/s11004-009-9229-1	PROPN
geusb-8353	171	2	bongajum	bongajum	NOUN
geusb-8353	171	3	,	,	PUNCT
geusb-8353	171	4	e.l	e.l	PROPN
geusb-8353	171	5	.	.	PROPN
geusb-8353	171	6	,	,	PUNCT
geusb-8353	171	7	boisvert	boisvert	PROPN
geusb-8353	171	8	,	,	PUNCT
geusb-8353	171	9	j.	j.	PROPN
geusb-8353	171	10	&	&	CCONJ
geusb-8353	171	11	sacchi	sacchi	PROPN
geusb-8353	171	12	,	,	PUNCT
geusb-8353	171	13	m.d	m.d	PROPN
geusb-8353	171	14	.	.	PROPN
geusb-8353	171	15	2013	2013	NUM
geusb-8353	171	16	:	:	PUNCT
geusb-8353	171	17	bayesian	bayesian	NOUN
geusb-8353	171	18	linearized	linearize	VERB
geusb-8353	171	19	seismic	seismic	ADJ
geusb-8353	171	20	inversion	inversion	NOUN
geusb-8353	171	21	with	with	ADP
geusb-8353	171	22	locally	locally	ADV
geusb-8353	171	23	varying	vary	VERB
geusb-8353	171	24	spatial	spatial	ADJ
geusb-8353	171	25	anisotropy	anisotropy	NOUN
geusb-8353	171	26	.	.	PUNCT
geusb-8353	172	1	journal	journal	PROPN
geusb-8353	172	2	of	of	ADP
geusb-8353	172	3	applied	apply	VERB
geusb-8353	172	4	geophysics	geophysic	NOUN
geusb-8353	172	5	88	88	NUM
geusb-8353	172	6	,	,	PUNCT
geusb-8353	172	7	31–41	31–41	NUM
geusb-8353	172	8	.	.	PUNCT
geusb-8353	173	1	https://doi.org/10.1016/j.jappgeo.2012.10.001	https://doi.org/10.1016/j.jappgeo.2012.10.001	PROPN
geusb-8353	173	2	cressie	cressie	PROPN
geusb-8353	173	3	,	,	PUNCT
geusb-8353	173	4	n.a.c	n.a.c	NOUN
geusb-8353	173	5	.	.	PUNCT
geusb-8353	173	6	1993	1993	NUM
geusb-8353	173	7	:	:	PUNCT
geusb-8353	173	8	statistics	statistic	NOUN
geusb-8353	173	9	for	for	ADP
geusb-8353	173	10	spatial	spatial	ADJ
geusb-8353	173	11	data	datum	NOUN
geusb-8353	173	12	.	.	PUNCT
geusb-8353	174	1	new	new	PROPN
geusb-8353	174	2	york	york	PROPN
geusb-8353	174	3	:	:	PUNCT
geusb-8353	174	4	wiley	wiley	PROPN
geusb-8353	174	5	.	.	PUNCT
geusb-8353	175	1	hansen	hansen	PROPN
geusb-8353	175	2	et	et	PROPN
geusb-8353	175	3	al	al	PROPN
geusb-8353	175	4	.	.	PROPN
geusb-8353	175	5	2013	2013	NUM
geusb-8353	175	6	:	:	PUNCT
geusb-8353	176	1	sippi	sippi	NOUN
geusb-8353	176	2	:	:	PUNCT
geusb-8353	176	3	a	a	DET
geusb-8353	176	4	matlab	matlab	PROPN
geusb-8353	176	5	toolbox	toolbox	NOUN
geusb-8353	176	6	for	for	ADP
geusb-8353	176	7	sampling	sample	VERB
geusb-8353	176	8	the	the	DET
geusb-8353	176	9	solution	solution	NOUN
geusb-8353	176	10	to	to	ADP
geusb-8353	176	11	inverse	inverse	NOUN
geusb-8353	176	12	problems	problem	NOUN
geusb-8353	176	13	with	with	ADP
geusb-8353	176	14	complex	complex	ADJ
geusb-8353	176	15	prior	prior	ADJ
geusb-8353	176	16	information	information	NOUN
geusb-8353	176	17	:	:	PUNCT
geusb-8353	176	18	part	part	NOUN
geusb-8353	176	19	1	1	NUM
geusb-8353	176	20	–	–	PUNCT
geusb-8353	176	21	methodology	methodology	NOUN
geusb-8353	176	22	.	.	PUNCT
geusb-8353	177	1	computational	computational	ADJ
geusb-8353	177	2	geoscience	geoscience	PROPN
geusb-8353	177	3	52	52	NUM
geusb-8353	177	4	,	,	PUNCT
geusb-8353	177	5	470–480	470–480	NUM
geusb-8353	177	6	.	.	PUNCT
geusb-8353	178	1	https://doi	https://doi	X
geusb-8353	178	2	.	.	PUNCT
geusb-8353	178	3	org/10.1016	org/10.1016	PROPN
geusb-8353	178	4	/	/	SYM
geusb-8353	178	5	j.cageo.2012.09.004	j.cageo.2012.09.004	PROPN
geusb-8353	178	6	higdon	higdon	PROPN
geusb-8353	178	7	d.	d.	PROPN
geusb-8353	178	8	,	,	PUNCT
geusb-8353	178	9	swall	swall	PROPN
geusb-8353	178	10	,	,	PUNCT
geusb-8353	178	11	j.	j.	PROPN
geusb-8353	178	12	,	,	PUNCT
geusb-8353	178	13	&	&	CCONJ
geusb-8353	178	14	kern	kern	PROPN
geusb-8353	178	15	,	,	PUNCT
geusb-8353	178	16	j.	j.	PROPN
geusb-8353	178	17	1999	1999	NUM
geusb-8353	178	18	:	:	PUNCT
geusb-8353	178	19	non	non	ADJ
geusb-8353	178	20	-	-	ADJ
geusb-8353	178	21	stationary	stationary	ADJ
geusb-8353	178	22	spatial	spatial	ADJ
geusb-8353	178	23	modeling	modeling	NOUN
geusb-8353	178	24	.	.	PUNCT
geusb-8353	179	1	in	in	ADP
geusb-8353	179	2	:	:	PUNCT
geusb-8353	179	3	bernado	bernado	NOUN
geusb-8353	179	4	,	,	PUNCT
geusb-8353	179	5	j.m	j.m	PROPN
geusb-8353	179	6	.	.	PROPN
geusb-8353	179	7	et	et	PROPN
geusb-8353	179	8	al	al	PROPN
geusb-8353	179	9	.	.	PROPN
geusb-8353	179	10	(	(	PUNCT
geusb-8353	179	11	eds	ed	NOUN
geusb-8353	179	12	)	)	PUNCT
geusb-8353	179	13	.	.	PUNCT
geusb-8353	180	1	bayesian	bayesian	NOUN
geusb-8353	180	2	statistics	statistic	NOUN
geusb-8353	180	3	(	(	PUNCT
geusb-8353	180	4	6	6	NUM
geusb-8353	180	5	ed	ed	NOUN
geusb-8353	180	6	.	.	PUNCT
geusb-8353	180	7	)	)	PUNCT
geusb-8353	180	8	.	.	PUNCT
geusb-8353	181	1	761–768	761–768	NUM
geusb-8353	181	2	.	.	PUNCT
geusb-8353	182	1	oxford	oxford	NOUN
geusb-8353	182	2	:	:	PUNCT
geusb-8353	182	3	oxford	oxford	PROPN
geusb-8353	182	4	university	university	PROPN
geusb-8353	182	5	press	press	NOUN
geusb-8353	182	6	.	.	PUNCT
geusb-8353	183	1	hornik	hornik	PROPN
geusb-8353	183	2	,	,	PUNCT
geusb-8353	183	3	k.	k.	PROPN
geusb-8353	183	4	1991	1991	NUM
geusb-8353	183	5	:	:	PUNCT
geusb-8353	184	1	approximation	approximation	NOUN
geusb-8353	184	2	capabilities	capability	NOUN
geusb-8353	184	3	of	of	ADP
geusb-8353	184	4	multilayer	multilayer	ADJ
geusb-8353	184	5	feedforward	feedforward	NOUN
geusb-8353	184	6	networks	network	NOUN
geusb-8353	184	7	.	.	PUNCT
geusb-8353	185	1	neural	neural	ADJ
geusb-8353	185	2	networks	network	NOUN
geusb-8353	185	3	4(2	4(2	NUM
geusb-8353	185	4	)	)	PUNCT
geusb-8353	185	5	,	,	PUNCT
geusb-8353	185	6	251–257	251–257	NUM
geusb-8353	185	7	.	.	PUNCT
geusb-8353	186	1	https://doi	https://doi	NOUN
geusb-8353	186	2	.	.	PUNCT
geusb-8353	186	3	org/10.1016/0893	org/10.1016/0893	PROPN
geusb-8353	186	4	-	-	PUNCT
geusb-8353	186	5	6080(91)90009	6080(91)90009	NUM
geusb-8353	186	6	-	-	PUNCT
geusb-8353	186	7	t	t	NOUN
geusb-8353	186	8	iandola	iandola	NOUN
geusb-8353	186	9	,	,	PUNCT
geusb-8353	186	10	f.n	f.n	PROPN
geusb-8353	186	11	.	.	PROPN
geusb-8353	186	12	,	,	PUNCT
geusb-8353	186	13	moskewicz	moskewicz	PROPN
geusb-8353	186	14	,	,	PUNCT
geusb-8353	186	15	m.w	m.w	PROPN
geusb-8353	186	16	.	.	PROPN
geusb-8353	186	17	,	,	PUNCT
geusb-8353	186	18	ashraf	ashraf	PROPN
geusb-8353	186	19	,	,	PUNCT
geusb-8353	186	20	k.	k.	PROPN
geusb-8353	186	21	,	,	PUNCT
geusb-8353	186	22	han	han	PROPN
geusb-8353	186	23	,	,	PUNCT
geusb-8353	186	24	s.	s.	PROPN
geusb-8353	186	25	,	,	PUNCT
geusb-8353	186	26	dally	dally	PROPN
geusb-8353	186	27	,	,	PUNCT
geusb-8353	186	28	w.j	w.j	PROPN
geusb-8353	186	29	.	.	PROPN
geusb-8353	186	30	&	&	CCONJ
geusb-8353	186	31	keutzer	keutzer	PROPN
geusb-8353	186	32	,	,	PUNCT
geusb-8353	186	33	k.	k.	PROPN
geusb-8353	186	34	2016	2016	NUM
geusb-8353	186	35	:	:	PUNCT
geusb-8353	186	36	squeezenet	squeezenet	NOUN
geusb-8353	186	37	:	:	PUNCT
geusb-8353	186	38	alexnet	alexnet	ADJ
geusb-8353	186	39	-	-	PUNCT
geusb-8353	186	40	level	level	NOUN
geusb-8353	186	41	accuracy	accuracy	NOUN
geusb-8353	186	42	with	with	ADP
geusb-8353	186	43	50x	50x	NUM
geusb-8353	186	44	fewer	few	ADJ
geusb-8353	186	45	parameters	parameter	NOUN
geusb-8353	186	46	and	and	CCONJ
geusb-8353	186	47	<	<	X
geusb-8353	186	48	1	1	NUM
geusb-8353	186	49	mb	mb	NOUN
geusb-8353	186	50	model	model	NOUN
geusb-8353	186	51	size	size	NOUN
geusb-8353	186	52	.	.	PUNCT
geusb-8353	187	1	arxiv	arxiv	PROPN
geusb-8353	187	2	,	,	PUNCT
geusb-8353	187	3	abs/1602.07360	abs/1602.07360	ADJ
geusb-8353	187	4	.	.	PUNCT
geusb-8353	188	1	https://doi	https://doi	X
geusb-8353	188	2	.	.	PUNCT
geusb-8353	188	3	org/10.48550	org/10.48550	PROPN
geusb-8353	188	4	/	/	SYM
geusb-8353	188	5	arxiv.1602.07360	arxiv.1602.07360	PROPN
geusb-8353	188	6	jo	jo	PROPN
geusb-8353	188	7	,	,	PUNCT
geusb-8353	188	8	h.	h.	PROPN
geusb-8353	188	9	&	&	CCONJ
geusb-8353	188	10	pyrcz	pyrcz	PROPN
geusb-8353	188	11	,	,	PUNCT
geusb-8353	188	12	m.j	m.j	PROPN
geusb-8353	188	13	.	.	PROPN
geusb-8353	188	14	2022	2022	NUM
geusb-8353	188	15	:	:	PUNCT
geusb-8353	188	16	automatic	automatic	ADJ
geusb-8353	188	17	semivariogram	semivariogram	NOUN
geusb-8353	188	18	modeling	modeling	NOUN
geusb-8353	188	19	by	by	ADP
geusb-8353	188	20	convolutional	convolutional	ADJ
geusb-8353	188	21	neural	neural	ADJ
geusb-8353	188	22	network	network	NOUN
geusb-8353	188	23	.	.	PUNCT
geusb-8353	189	1	mathematical	mathematical	ADJ
geusb-8353	189	2	geosciences	geoscience	NOUN
geusb-8353	189	3	54(1	54(1	NUM
geusb-8353	189	4	)	)	PUNCT
geusb-8353	189	5	,	,	PUNCT
geusb-8353	189	6	177–205	177–205	NUM
geusb-8353	189	7	.	.	PUNCT
geusb-8353	190	1	https://doi.org/10.1007/s11004-021-09962-w	https://doi.org/10.1007/s11004-021-09962-w	PROPN
geusb-8353	190	2	kohonen	kohonen	PROPN
geusb-8353	190	3	,	,	PUNCT
geusb-8353	190	4	t.	t.	PROPN
geusb-8353	190	5	1991	1991	NUM
geusb-8353	190	6	:	:	PUNCT
geusb-8353	190	7	self	self	NOUN
geusb-8353	190	8	-	-	PUNCT
geusb-8353	190	9	organising	organise	VERB
geusb-8353	190	10	maps	map	NOUN
geusb-8353	190	11	:	:	PUNCT
geusb-8353	190	12	ophmization	ophmization	NOUN
geusb-8353	190	13	approaches	approach	NOUN
geusb-8353	190	14	.	.	PUNCT
geusb-8353	191	1	in	in	ADP
geusb-8353	191	2	:	:	PUNCT
geusb-8353	191	3	kohonen	kohonen	PROPN
geusb-8353	191	4	,	,	PUNCT
geusb-8353	191	5	t.	t.	PROPN
geusb-8353	191	6	,	,	PUNCT
geusb-8353	191	7	et	et	PROPN
geusb-8353	191	8	al	al	PROPN
geusb-8353	191	9	.	.	PUNCT
geusb-8353	192	1	(	(	PUNCT
geusb-8353	192	2	eds	ed	NOUN
geusb-8353	192	3	):	):	PUNCT
geusb-8353	192	4	artificial	artificial	ADJ
geusb-8353	192	5	neural	neural	ADJ
geusb-8353	192	6	networks	network	NOUN
geusb-8353	192	7	.	.	PUNCT
geusb-8353	193	1	pp	pp	ADV
geusb-8353	193	2	.	.	PUNCT
geusb-8353	194	1	981–990	981–990	NUM
geusb-8353	194	2	.	.	PUNCT
geusb-8353	195	1	amsterdam	amsterdam	PROPN
geusb-8353	195	2	:	:	PUNCT
geusb-8353	195	3	north	north	NOUN
geusb-8353	195	4	-	-	PUNCT
geusb-8353	195	5	holland	holland	NOUN
geusb-8353	195	6	.	.	PUNCT
geusb-8353	196	1	https://doi.org/10.1016/b978-0-444-89178-5.50003-8	https://doi.org/10.1016/b978-0-444-89178-5.50003-8	PRON
geusb-8353	196	2	li	li	PROPN
geusb-8353	196	3	,	,	PUNCT
geusb-8353	196	4	y.	y.	PROPN
geusb-8353	196	5	,	,	PUNCT
geusb-8353	196	6	baorong	baorong	PROPN
geusb-8353	196	7	,	,	PUNCT
geusb-8353	196	8	z.	z.	PROPN
geusb-8353	196	9	,	,	PUNCT
geusb-8353	196	10	xiaohong	xiaohong	PROPN
geusb-8353	196	11	,	,	PUNCT
geusb-8353	196	12	x.	x.	PROPN
geusb-8353	196	13	&	&	CCONJ
geusb-8353	196	14	zijun	zijun	PROPN
geusb-8353	196	15	,	,	PUNCT
geusb-8353	196	16	l.	l.	PROPN
geusb-8353	196	17	2022	2022	NUM
geusb-8353	196	18	:	:	PUNCT
geusb-8353	196	19	application	application	NOUN
geusb-8353	196	20	of	of	ADP
geusb-8353	196	21	a	a	DET
geusb-8353	196	22	semivariogram	semivariogram	NOUN
geusb-8353	196	23	based	base	VERB
geusb-8353	196	24	on	on	ADP
geusb-8353	196	25	a	a	DET
geusb-8353	196	26	deep	deep	ADJ
geusb-8353	196	27	neural	neural	ADJ
geusb-8353	196	28	network	network	NOUN
geusb-8353	196	29	to	to	ADP
geusb-8353	196	30	ordinary	ordinary	ADJ
geusb-8353	196	31	kriging	krige	VERB
geusb-8353	196	32	interpolation	interpolation	NOUN
geusb-8353	196	33	of	of	ADP
geusb-8353	196	34	elevation	elevation	NOUN
geusb-8353	196	35	data	datum	NOUN
geusb-8353	196	36	.	.	PUNCT
geusb-8353	197	1	plos	plos	PROPN
geusb-8353	197	2	one	one	NUM
geusb-8353	197	3	17(4	17(4	NUM
geusb-8353	197	4	)	)	PUNCT
geusb-8353	197	5	,	,	PUNCT
geusb-8353	197	6	e0266942	e0266942	NOUN
geusb-8353	197	7	.	.	PUNCT
geusb-8353	198	1	https://doi	https://doi	X
geusb-8353	198	2	.	.	PUNCT
geusb-8353	199	1	org/10.1371	org/10.1371	PROPN
geusb-8353	199	2	/	/	SYM
geusb-8353	199	3	journal.pone.0266942	journal.pone.0266942	PROPN
geusb-8353	199	4	madsen	madsen	PROPN
geusb-8353	199	5	,	,	PUNCT
geusb-8353	199	6	r.b	r.b	PROPN
geusb-8353	199	7	.	.	PROPN
geusb-8353	199	8	,	,	PUNCT
geusb-8353	199	9	hansen	hansen	PROPN
geusb-8353	199	10	,	,	PUNCT
geusb-8353	199	11	t.m	t.m	PROPN
geusb-8353	199	12	.	.	PROPN
geusb-8353	199	13	&	&	CCONJ
geusb-8353	199	14	omre	omre	PROPN
geusb-8353	199	15	,	,	PUNCT
geusb-8353	199	16	h.	h.	PROPN
geusb-8353	199	17	2020	2020	NUM
geusb-8353	199	18	:	:	PUNCT
geusb-8353	199	19	estimation	estimation	NOUN
geusb-8353	199	20	of	of	ADP
geusb-8353	199	21	a	a	DET
geusb-8353	199	22	non	non	ADJ
geusb-8353	199	23	stationary	stationary	ADJ
geusb-8353	199	24	prior	prior	ADJ
geusb-8353	199	25	covariance	covariance	NOUN
geusb-8353	199	26	from	from	ADP
geusb-8353	199	27	seismic	seismic	ADJ
geusb-8353	199	28	data	datum	NOUN
geusb-8353	199	29	.	.	PUNCT
geusb-8353	200	1	geophysical	geophysical	ADJ
geusb-8353	200	2	prospecting	prospecting	ADJ
geusb-8353	200	3	68(2	68(2	NOUN
geusb-8353	200	4	)	)	PUNCT
geusb-8353	200	5	,	,	PUNCT
geusb-8353	200	6	393–410	393–410	NUM
geusb-8353	200	7	.	.	PUNCT
geusb-8353	201	1	https://doi.org/10.1111/1365-2478.12848	https://doi.org/10.1111/1365-2478.12848	PROPN
geusb-8353	201	2	madsen	madsen	PROPN
geusb-8353	201	3	,	,	PUNCT
geusb-8353	201	4	r.b	r.b	PROPN
geusb-8353	201	5	.	.	PROPN
geusb-8353	201	6	,	,	PUNCT
geusb-8353	201	7	høyer	høyer	PROPN
geusb-8353	201	8	,	,	PUNCT
geusb-8353	201	9	a.s	a.s	PROPN
geusb-8353	201	10	.	.	PROPN
geusb-8353	201	11	,	,	PUNCT
geusb-8353	201	12	andersen	andersen	PROPN
geusb-8353	201	13	,	,	PUNCT
geusb-8353	201	14	l.t	l.t	PROPN
geusb-8353	201	15	.	.	PROPN
geusb-8353	201	16	,	,	PUNCT
geusb-8353	201	17	møller	møller	NOUN
geusb-8353	201	18	,	,	PUNCT
geusb-8353	201	19	i.	i.	PROPN
geusb-8353	201	20	&	&	CCONJ
geusb-8353	201	21	hansen	hansen	PROPN
geusb-8353	201	22	,	,	PUNCT
geusb-8353	201	23	t.m	t.m	PROPN
geusb-8353	201	24	.	.	PROPN
geusb-8353	201	25	2022	2022	NUM
geusb-8353	201	26	:	:	PUNCT
geusb-8353	201	27	geology	geology	NOUN
geusb-8353	201	28	-	-	PUNCT
geusb-8353	201	29	driven	drive	VERB
geusb-8353	201	30	modelling	modelling	NOUN
geusb-8353	201	31	:	:	PUNCT
geusb-8353	201	32	a	a	DET
geusb-8353	201	33	new	new	ADJ
geusb-8353	201	34	probabilistic	probabilistic	ADJ
geusb-8353	201	35	approach	approach	NOUN
geusb-8353	201	36	for	for	ADP
geusb-8353	201	37	incorporating	incorporate	VERB
geusb-8353	201	38	uncertain	uncertain	ADJ
geusb-8353	201	39	geological	geological	ADJ
geusb-8353	201	40	interpretations	interpretation	NOUN
geusb-8353	201	41	in	in	ADP
geusb-8353	201	42	3d	3d	PROPN
geusb-8353	201	43	geological	geological	ADJ
geusb-8353	201	44	modelling	modelling	NOUN
geusb-8353	201	45	.	.	PUNCT
geusb-8353	202	1	engineering	engineering	NOUN
geusb-8353	202	2	geology	geology	NOUN
geusb-8353	202	3	309	309	NUM
geusb-8353	202	4	,	,	PUNCT
geusb-8353	202	5	106833	106833	NUM
geusb-8353	202	6	.	.	PUNCT
geusb-8353	203	1	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
geusb-8353	203	2	.	.	PUNCT
geusb-8353	204	1	enggeo.2022.106833	enggeo.2022.106833	VERB
geusb-8353	204	2	mardia	mardia	PROPN
geusb-8353	204	3	,	,	PUNCT
geusb-8353	204	4	k.v	k.v	PROPN
geusb-8353	204	5	.	.	PROPN
geusb-8353	204	6	1990	1990	NUM
geusb-8353	204	7	:	:	PUNCT
geusb-8353	204	8	maximum	maximum	ADJ
geusb-8353	204	9	likelihood	likelihood	NOUN
geusb-8353	204	10	estimation	estimation	NOUN
geusb-8353	204	11	for	for	ADP
geusb-8353	204	12	spatial	spatial	ADJ
geusb-8353	204	13	models	model	NOUN
geusb-8353	204	14	in	in	ADP
geusb-8353	204	15	spatial	spatial	ADJ
geusb-8353	204	16	statistics	statistic	NOUN
geusb-8353	204	17	:	:	PUNCT
geusb-8353	204	18	past	past	ADJ
geusb-8353	204	19	,	,	PUNCT
geusb-8353	204	20	present	present	ADJ
geusb-8353	204	21	,	,	PUNCT
geusb-8353	204	22	and	and	CCONJ
geusb-8353	204	23	future	future	NOUN
geusb-8353	204	24	.	.	PUNCT
geusb-8353	205	1	203–253	203–253	NUM
geusb-8353	205	2	.	.	PUNCT
geusb-8353	206	1	https://doi	https://doi	X
geusb-8353	206	2	.	.	PUNCT
geusb-8353	207	1	org/10.1068	org/10.1068	ADJ
geusb-8353	207	2	/	/	SYM
geusb-8353	207	3	a270615	a270615	ADJ
geusb-8353	207	4	matheron	matheron	NOUN
geusb-8353	207	5	,	,	PUNCT
geusb-8353	207	6	g.	g.	PROPN
geusb-8353	207	7	1963	1963	NUM
geusb-8353	207	8	:	:	PUNCT
geusb-8353	208	1	principles	principle	NOUN
geusb-8353	208	2	of	of	ADP
geusb-8353	208	3	geostatistics	geostatistic	NOUN
geusb-8353	208	4	.	.	PUNCT
geusb-8353	209	1	economic	economic	ADJ
geusb-8353	209	2	geology	geology	NOUN
geusb-8353	209	3	58(8	58(8	NUM
geusb-8353	209	4	)	)	PUNCT
geusb-8353	209	5	,	,	PUNCT
geusb-8353	209	6	1246–1266	1246–1266	ADJ
geusb-8353	209	7	.	.	PUNCT
geusb-8353	210	1	https://doi.org/10.2113/gsecongeo.58.8.1246	https://doi.org/10.2113/gsecongeo.58.8.1246	PROPN
geusb-8353	210	2	pereira	pereira	PROPN
geusb-8353	210	3	,	,	PUNCT
geusb-8353	210	4	â	â	PROPN
geusb-8353	210	5	.	.	PROPN
geusb-8353	210	6	et	et	PROPN
geusb-8353	210	7	al	al	PROPN
geusb-8353	210	8	.	.	PROPN
geusb-8353	210	9	2023	2023	NUM
geusb-8353	210	10	:	:	PUNCT
geusb-8353	210	11	updating	update	VERB
geusb-8353	210	12	local	local	ADJ
geusb-8353	210	13	anisotropies	anisotropy	NOUN
geusb-8353	210	14	with	with	ADP
geusb-8353	210	15	template	template	NOUN
geusb-8353	210	16	matching	match	VERB
geusb-8353	210	17	during	during	ADP
geusb-8353	210	18	geostatistical	geostatistical	ADJ
geusb-8353	210	19	seismic	seismic	ADJ
geusb-8353	210	20	inversion	inversion	NOUN
geusb-8353	210	21	.	.	PUNCT
geusb-8353	211	1	mathematical	mathematical	ADJ
geusb-8353	211	2	geosciences	geoscience	NOUN
geusb-8353	211	3	55(4	55(4	NOUN
geusb-8353	211	4	)	)	PUNCT
geusb-8353	211	5	,	,	PUNCT
geusb-8353	211	6	497–519	497–519	NUM
geusb-8353	211	7	,	,	PUNCT
geusb-8353	211	8	https://doi.org/10.1007/s11004-023-10051-3	https://doi.org/10.1007/s11004-023-10051-3	NUM
geusb-8353	212	1	stisen	stisen	PROPN
geusb-8353	212	2	,	,	PUNCT
geusb-8353	212	3	s.	s.	PROPN
geusb-8353	212	4	,	,	PUNCT
geusb-8353	212	5	ondracek	ondracek	PROPN
geusb-8353	212	6	,	,	PUNCT
geusb-8353	212	7	m.	m.	NOUN
geusb-8353	212	8	,	,	PUNCT
geusb-8353	212	9	troldborg	troldborg	PROPN
geusb-8353	212	10	,	,	PUNCT
geusb-8353	212	11	l.	l.	PROPN
geusb-8353	212	12	,	,	PUNCT
geusb-8353	212	13	schneider	schneider	PROPN
geusb-8353	212	14	,	,	PUNCT
geusb-8353	212	15	r.j.m	r.j.m	NOUN
geusb-8353	212	16	.	.	PROPN
geusb-8353	212	17	&	&	CCONJ
geusb-8353	212	18	til	til	ADV
geusb-8353	212	19	,	,	PUNCT
geusb-8353	212	20	m.j.v	m.j.v	INTJ
geusb-8353	212	21	.	.	PUNCT
geusb-8353	213	1	2020	2020	NUM
geusb-8353	213	2	:	:	PUNCT
geusb-8353	214	1	national	national	ADJ
geusb-8353	214	2	vandressource	vandressource	NOUN
geusb-8353	214	3	model	model	NOUN
geusb-8353	214	4	.	.	PUNCT
geusb-8353	215	1	modelopstilling	modelopstille	VERB
geusb-8353	215	2	og	og	PROPN
geusb-8353	215	3	kalibrering	kalibrere	VERB
geusb-8353	215	4	af	af	PROPN
geusb-8353	215	5	dk	dk	PROPN
geusb-8353	215	6	-	-	NOUN
geusb-8353	215	7	model	model	NOUN
geusb-8353	215	8	2019	2019	NUM
geusb-8353	215	9	.	.	PUNCT
geusb-8353	216	1	danmarks	danmark	NOUN
geusb-8353	216	2	og	og	PROPN
geusb-8353	216	3	grønlands	grønland	NOUN
geusb-8353	216	4	geologiske	geologiske	PROPN
geusb-8353	216	5	undersøgelse	undersøgelse	PROPN
geusb-8353	216	6	rapport	rapport	PROPN
geusb-8353	216	7	2019/31	2019/31	PROPN
geusb-8353	216	8	,	,	PUNCT
geusb-8353	216	9	23–27	23–27	NUM
geusb-8353	216	10	,	,	PUNCT
geusb-8353	216	11	https://doi.org/10.22008/gpub/32631	https://doi.org/10.22008/gpub/32631	PROPN
geusb-8353	216	12	zhou	zhou	PROPN
geusb-8353	216	13	,	,	PUNCT
geusb-8353	216	14	l.	l.	PROPN
geusb-8353	216	15	,	,	PUNCT
geusb-8353	216	16	hongming	hongming	NOUN
geusb-8353	216	17	,	,	PUNCT
geusb-8353	216	18	p.	p.	PROPN
geusb-8353	216	19	,	,	PUNCT
geusb-8353	216	20	wang	wang	PROPN
geusb-8353	216	21	,	,	PUNCT
geusb-8353	216	22	f.	f.	PROPN
geusb-8353	216	23	,	,	PUNCT
geusb-8353	216	24	zhiqiang	zhiqiang	PROPN
geusb-8353	216	25	,	,	PUNCT
geusb-8353	216	26	h.	h.	PROPN
geusb-8353	216	27	&	&	CCONJ
geusb-8353	216	28	wang	wang	PROPN
geusb-8353	216	29	,	,	PUNCT
geusb-8353	216	30	l.	l.	PROPN
geusb-8353	216	31	2017	2017	NUM
geusb-8353	216	32	:	:	PUNCT
geusb-8353	216	33	the	the	DET
geusb-8353	216	34	expressive	expressive	ADJ
geusb-8353	216	35	power	power	NOUN
geusb-8353	216	36	of	of	ADP
geusb-8353	216	37	neural	neural	ADJ
geusb-8353	216	38	networks	network	NOUN
geusb-8353	216	39	:	:	PUNCT
geusb-8353	216	40	a	a	DET
geusb-8353	216	41	view	view	NOUN
geusb-8353	216	42	from	from	ADP
geusb-8353	216	43	the	the	DET
geusb-8353	216	44	width	width	NOUN
geusb-8353	216	45	.	.	PUNCT
geusb-8353	217	1	in	in	ADP
geusb-8353	217	2	:	:	PUNCT
geusb-8353	217	3	guyon	guyon	NOUN
geusb-8353	217	4	,	,	PUNCT
geusb-8353	217	5	i.	i.	PROPN
geusb-8353	217	6	et	et	PROPN
geusb-8353	217	7	al	al	PROPN
geusb-8353	217	8	.	.	PROPN
geusb-8353	217	9	(	(	PUNCT
geusb-8353	217	10	eds	ed	NOUN
geusb-8353	217	11	.	.	PUNCT
geusb-8353	217	12	)	)	PUNCT
geusb-8353	217	13	advances	advance	NOUN
geusb-8353	217	14	in	in	ADP
geusb-8353	217	15	neural	neural	ADJ
geusb-8353	217	16	information	information	NOUN
geusb-8353	217	17	processing	processing	NOUN
geusb-8353	217	18	systems	system	NOUN
geusb-8353	217	19	30	30	NUM
geusb-8353	217	20	,	,	PUNCT
geusb-8353	217	21	7–8	7–8	NOUN
geusb-8353	217	22	.	.	PUNCT
geusb-8353	217	23	new	new	PROPN
geusb-8353	217	24	york	york	PROPN
geusb-8353	217	25	:	:	PUNCT
geusb-8353	217	26	curran	curran	PROPN
geusb-8353	217	27	associates	associates	PROPN
geusb-8353	217	28	inc	inc	PROPN
geusb-8353	217	29	.	.	PROPN
geusb-8353	217	30	https://proceedings.neurips.cc/	https://proceedings.neurips.cc/	PROPN
geusb-8353	217	31	paper_files	paper_files	PROPN
geusb-8353	217	32	/	/	SYM
geusb-8353	217	33	paper/2017	paper/2017	ADJ
geusb-8353	217	34	/	/	PUNCT
geusb-8353	217	35	file/32cbf687880eb1674a07bf717761dd3a	file/32cbf687880eb1674a07bf717761dd3a	NOUN
geusb-8353	217	36	-	-	NOUN
geusb-8353	217	37	paper.pdf	paper.pdf	NOUN
geusb-8353	217	38	(	(	PUNCT
geusb-8353	217	39	accessed	access	VERB
geusb-8353	217	40	october	october	PROPN
geusb-8353	217	41	2023	2023	NUM
geusb-8353	217	42	)	)	PUNCT
geusb-8353	217	43	https://doi.org/10.34194/geusb.v53.8353	https://doi.org/10.34194/geusb.v53.8353	PROPN
geusb-8353	217	44	http://www.geusbulletin.org	http://www.geusbulletin.org	PUNCT
geusb-8353	217	45	https://doi.org/10.1016/j.cageo.2010.03.021	https://doi.org/10.1016/j.cageo.2010.03.021	PROPN
geusb-8353	217	46	https://doi.org/10.1016/j.cageo.2010.03.021	https://doi.org/10.1016/j.cageo.2010.03.021	NOUN
geusb-8353	217	47	https://doi.org/10.1007/s11004-009-9229-1	https://doi.org/10.1007/s11004-009-9229-1	PRON
geusb-8353	217	48	https://doi.org/10.1016/j.jappgeo.2012.10.001	https://doi.org/10.1016/j.jappgeo.2012.10.001	VERB
geusb-8353	217	49	https://doi.org/10.1016/j.cageo.2012.09.004	https://doi.org/10.1016/j.cageo.2012.09.004	NOUN
geusb-8353	217	50	https://doi.org/10.1016/j.cageo.2012.09.004	https://doi.org/10.1016/j.cageo.2012.09.004	NOUN
geusb-8353	217	51	https://doi.org/10.1016/0893-6080(91)90009-t	https://doi.org/10.1016/0893-6080(91)90009-t	NOUN
geusb-8353	217	52	https://doi.org/10.1016/0893-6080(91)90009-t	https://doi.org/10.1016/0893-6080(91)90009-t	NOUN
geusb-8353	217	53	https://doi.org/10.48550/arxiv.1602.07360	https://doi.org/10.48550/arxiv.1602.07360	PROPN
geusb-8353	218	1	https://doi.org/10.48550/arxiv.1602.07360	https://doi.org/10.48550/arxiv.1602.07360	PROPN
geusb-8353	218	2	https://doi.org/10.1007/s11004-021-09962-w	https://doi.org/10.1007/s11004-021-09962-w	NOUN
geusb-8353	218	3	https://doi.org/10.1016/b978-0-444-89178-5.50003-8	https://doi.org/10.1016/b978-0-444-89178-5.50003-8	PRON
geusb-8353	218	4	https://doi.org/10.1371/journal.pone.0266942	https://doi.org/10.1371/journal.pone.0266942	VERB
geusb-8353	218	5	https://doi.org/10.1371/journal.pone.0266942	https://doi.org/10.1371/journal.pone.0266942	NOUN
geusb-8353	218	6	https://doi.org/10.1111/1365-2478.12848	https://doi.org/10.1111/1365-2478.12848	PROPN
geusb-8353	218	7	https://doi.org/10.1016/j.enggeo.2022.106833	https://doi.org/10.1016/j.enggeo.2022.106833	PROPN
geusb-8353	218	8	https://doi.org/10.1016/j.enggeo.2022.106833	https://doi.org/10.1016/j.enggeo.2022.106833	PROPN
geusb-8353	218	9	https://doi.org/10.1068/a270615	https://doi.org/10.1068/a270615	PROPN
geusb-8353	218	10	https://doi.org/10.1068/a270615	https://doi.org/10.1068/a270615	PROPN
geusb-8353	218	11	https://doi.org/10.2113/gsecongeo.58.8.1246	https://doi.org/10.2113/gsecongeo.58.8.1246	NOUN
geusb-8353	218	12	https://doi.org/10.1007/s11004-023-10051-3	https://doi.org/10.1007/s11004-023-10051-3	ADP
geusb-8353	218	13	https://doi.org/10.22008/gpub/32631	https://doi.org/10.22008/gpub/32631	PROPN
geusb-8353	218	14	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	PROPN
geusb-8353	218	15	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	PROPN
geusb-8353	218	16	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	https://proceedings.neurips.cc/paper_files/paper/2017/file/32cbf687880eb1674a07bf717761dd3a-paper.pdf	PROPN
geusb-8353	218	17	machine	machine	NOUN
geusb-8353	218	18	learning	learning	NOUN
geusb-8353	218	19	-	-	PUNCT
geusb-8353	218	20	based	base	VERB
geusb-8353	218	21	estimation	estimation	NOUN
geusb-8353	218	22	and	and	CCONJ
geusb-8353	218	23	clustering	clustering	NOUN
geusb-8353	218	24	of	of	ADP
geusb-8353	218	25	statistics	statistic	NOUN
geusb-8353	218	26	within	within	ADP
geusb-8353	218	27	stratigraphic	stratigraphic	ADJ
geusb-8353	218	28	models	model	NOUN
geusb-8353	218	29	as	as	SCONJ
geusb-8353	218	30	exemplified	exemplify	VERB
geusb-8353	218	31	in	in	ADP
geusb-8353	218	32	denmark	denmark	NOUN
geusb-8353	218	33	introduction	introduction	NOUN
geusb-8353	218	34	required	require	VERB
geusb-8353	218	35	resources	resource	NOUN
geusb-8353	218	36	methodological	methodological	ADJ
geusb-8353	218	37	protocols	protocol	NOUN
geusb-8353	218	38	producing	produce	VERB
geusb-8353	218	39	training	training	NOUN
geusb-8353	218	40	,	,	PUNCT
geusb-8353	218	41	validation	validation	NOUN
geusb-8353	218	42	and	and	CCONJ
geusb-8353	218	43	test	test	NOUN
geusb-8353	218	44	data	datum	NOUN
geusb-8353	218	45	training	train	VERB
geusb-8353	218	46	the	the	DET
geusb-8353	218	47	network	network	NOUN
geusb-8353	218	48	validation	validation	NOUN
geusb-8353	218	49	validation	validation	NOUN
geusb-8353	218	50	on	on	ADP
geusb-8353	218	51	synthetic	synthetic	ADJ
geusb-8353	218	52	data	datum	NOUN
geusb-8353	218	53	validation	validation	NOUN
geusb-8353	218	54	by	by	ADP
geusb-8353	218	55	application	application	NOUN
geusb-8353	218	56	of	of	ADP
geusb-8353	218	57	the	the	DET
geusb-8353	218	58	method	method	NOUN
geusb-8353	218	59	discussion	discussion	NOUN
geusb-8353	218	60	and	and	CCONJ
geusb-8353	218	61	outlook	outlook	NOUN
geusb-8353	218	62	acknowledgements	acknowledgement	NOUN
geusb-8353	218	63	additional	additional	ADJ
geusb-8353	218	64	information	information	NOUN
geusb-8353	218	65	funding	funding	NOUN
geusb-8353	218	66	statement	statement	NOUN
geusb-8353	218	67	author	author	NOUN
geusb-8353	218	68	contributions	contribution	VERB
geusb-8353	218	69	competing	compete	VERB
geusb-8353	218	70	interests	interest	NOUN
geusb-8353	218	71	additional	additional	ADJ
geusb-8353	218	72	files	file	NOUN
geusb-8353	218	73	references	reference	NOUN
geusb-8353	218	74	figures	figure	VERB
geusb-8353	218	75	fig	fig	NOUN
geusb-8353	218	76	.	.	PUNCT
geusb-8353	219	1	1	1	NUM
geusb-8353	219	2	synthetic	synthetic	ADJ
geusb-8353	219	3	training	training	NOUN
geusb-8353	219	4	data	datum	NOUN
geusb-8353	219	5	produced	produce	VERB
geusb-8353	219	6	using	use	VERB
geusb-8353	219	7	gaussian	gaussian	ADJ
geusb-8353	219	8	semivariance	semivariance	NOUN
geusb-8353	219	9	models	model	NOUN
geusb-8353	219	10	with	with	ADP
geusb-8353	219	11	random	random	ADJ
geusb-8353	219	12	sill	sill	ADJ
geusb-8353	219	13	and	and	CCONJ
geusb-8353	219	14	range	range	VERB
geusb-8353	219	15	,	,	PUNCT
geusb-8353	219	16	with	with	ADP
geusb-8353	219	17	a	a	DET
geusb-8353	219	18	component	component	NOUN
geusb-8353	219	19	of	of	ADP
geusb-8353	219	20	noise	noise	NOUN
geusb-8353	219	21	.	.	PUNCT
geusb-8353	220	1	the	the	DET
geusb-8353	220	2	circles	circle	NOUN
geusb-8353	220	3	show	show	VERB
geusb-8353	220	4	120	120	NUM
geusb-8353	220	5	randomly	randomly	ADV
geusb-8353	220	6	drawn	draw	VERB
geusb-8353	220	7	points	point	NOUN
geusb-8353	220	8	from	from	ADP
geusb-8353	220	9	each	each	DET
geusb-8353	220	10	realisation	realisation	NOUN
geusb-8353	220	11	.	.	PUNCT
geusb-8353	221	1	shading	shading	NOUN
geusb-8353	221	2	:	:	PUNCT
geusb-8353	221	3	smallest	small	ADJ
geusb-8353	221	4	values	value	NOUN
geusb-8353	221	5	are	be	AUX
geusb-8353	221	6	shown	show	VERB
geusb-8353	221	7	in	in	ADP
geusb-8353	221	8	blue	blue	ADJ
geusb-8353	221	9	and	and	CCONJ
geusb-8353	221	10	largest	large	ADJ
geusb-8353	221	11	values	value	NOUN
geusb-8353	221	12	are	be	AUX
geusb-8353	221	13	shown	show	VERB
geusb-8353	221	14	in	in	ADP
geusb-8353	221	15	yellow	yellow	ADJ
geusb-8353	221	16	,	,	PUNCT
geusb-8353	221	17	with	with	ADP
geusb-8353	221	18	in	in	ADP
geusb-8353	221	19	-	-	PUNCT
geusb-8353	221	20	between	between	ADP
geusb-8353	221	21	values	value	NOUN
geusb-8353	221	22	shown	show	VERB
geusb-8353	221	23	in	in	ADP
geusb-8353	221	24	green	green	PROPN
geusb-8353	221	25	.	.	PUNCT
geusb-8353	222	1	fig	fig	NOUN
geusb-8353	222	2	.	.	PUNCT
geusb-8353	223	1	1	1	NUM
geusb-8353	223	2	synthetic	synthetic	ADJ
geusb-8353	223	3	training	training	NOUN
geusb-8353	223	4	data	datum	NOUN
geusb-8353	223	5	produced	produce	VERB
geusb-8353	223	6	using	use	VERB
geusb-8353	223	7	gaussian	gaussian	ADJ
geusb-8353	223	8	semivariance	semivariance	NOUN
geusb-8353	223	9	models	model	NOUN
geusb-8353	223	10	with	with	ADP
geusb-8353	223	11	random	random	ADJ
geusb-8353	223	12	sill	sill	ADJ
geusb-8353	223	13	and	and	CCONJ
geusb-8353	223	14	range	range	VERB
geusb-8353	223	15	,	,	PUNCT
geusb-8353	223	16	with	with	ADP
geusb-8353	223	17	a	a	DET
geusb-8353	223	18	component	component	NOUN
geusb-8353	223	19	of	of	ADP
geusb-8353	223	20	noise	noise	NOUN
geusb-8353	223	21	.	.	PUNCT
geusb-8353	224	1	the	the	DET
geusb-8353	224	2	circles	circle	NOUN
geusb-8353	224	3	show	show	VERB
geusb-8353	224	4	120	120	NUM
geusb-8353	224	5	randomly	randomly	ADV
geusb-8353	224	6	drawn	draw	VERB
geusb-8353	224	7	points	point	NOUN
geusb-8353	224	8	from	from	ADP
geusb-8353	224	9	each	each	DET
geusb-8353	224	10	realisation	realisation	NOUN
geusb-8353	224	11	.	.	PUNCT
geusb-8353	225	1	shading	shading	NOUN
geusb-8353	225	2	:	:	PUNCT
geusb-8353	225	3	smallest	small	ADJ
geusb-8353	225	4	values	value	NOUN
geusb-8353	225	5	are	be	AUX
geusb-8353	225	6	shown	show	VERB
geusb-8353	225	7	in	in	ADP
geusb-8353	225	8	blue	blue	ADJ
geusb-8353	225	9	and	and	CCONJ
geusb-8353	225	10	largest	large	ADJ
geusb-8353	225	11	values	value	NOUN
geusb-8353	225	12	are	be	AUX
geusb-8353	225	13	shown	show	VERB
geusb-8353	225	14	in	in	ADP
geusb-8353	225	15	yellow	yellow	ADJ
geusb-8353	225	16	,	,	PUNCT
geusb-8353	225	17	with	with	ADP
geusb-8353	225	18	in	in	ADP
geusb-8353	225	19	-	-	PUNCT
geusb-8353	225	20	between	between	ADP
geusb-8353	225	21	values	value	NOUN
geusb-8353	225	22	shown	show	VERB
geusb-8353	225	23	in	in	ADP
geusb-8353	225	24	green	green	PROPN
geusb-8353	225	25	.	.	PUNCT
geusb-8353	226	1	fig	fig	NOUN
geusb-8353	226	2	.	.	PUNCT
geusb-8353	227	1	3	3	NUM
geusb-8353	227	2	synthetic	synthetic	ADJ
geusb-8353	227	3	examples	example	NOUN
geusb-8353	227	4	:	:	PUNCT
geusb-8353	227	5	8	8	NUM
geusb-8353	227	6	realisations	realisation	NOUN
geusb-8353	227	7	are	be	AUX
geusb-8353	227	8	shown	show	VERB
geusb-8353	227	9	–	–	PUNCT
geusb-8353	227	10	each	each	PRON
geusb-8353	227	11	of	of	ADP
geusb-8353	227	12	their	their	PRON
geusb-8353	227	13	own	own	ADJ
geusb-8353	227	14	gaussian	gaussian	ADJ
geusb-8353	227	15	semivariogram	semivariogram	NOUN
geusb-8353	227	16	model	model	NOUN
geusb-8353	227	17	with	with	ADP
geusb-8353	227	18	some	some	DET
geusb-8353	227	19	component	component	NOUN
geusb-8353	227	20	of	of	ADP
geusb-8353	227	21	noise	noise	NOUN
geusb-8353	227	22	.	.	PUNCT
geusb-8353	228	1	the	the	DET
geusb-8353	228	2	semivariance	semivariance	NOUN