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New constraints for site-effect characterization from seismic noise
analysis in southern Italy. San Fele case study

Margherita Corciulo1,2 *, Paola Cusano3, Simona Petrosino3

1Analisi e Monitoraggio del Rischio Ambientale (AMRA) S.c.ar.l., Napoli, Italy
2 Laboratoire de Géophysique Interne et Tectonophysique, Université J. Fourier, Grenoble, France
3 Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Napoli, Italy

ANNALS OF GEOPHYSICS, 53, 4, 2010; doi: 10.4401/ag-4667

ABSTRACT

In the framework of  ground-motion amplification analysis for southern
Italy, the main target of  this study is to provide new constraints on one-
dimensional, shallow-velocity profiles for a site in the San Fele area
near the city of  Potenza (southern Italy) where a permanent Irpinia
Seismic Network (ISNet) seismic station is installed. Ambient noise
vibrations were recorded during a seismic survey in San Fele, and the
data acquired were used to define the shallow shear-wave velocity
profiles and thicknesses of  the shallow soil layers, through analysis of
the dispersion characteristics of  the surface waves. Single station and
array techniques were used to obtain robust results, which show
relatively flat curves of  the H/V spectral ratios and variations in shear-
wave velocities confined to the first 50 m in depth. On the basis of  these
results for the San Fele site, the present study aims to delineate a
standard procedure that can be systematically applied to all of  the other
ISNet stations to improve site characterization. This will allow more
accurate evaluation of  peak ground-motion quantities (e.g. peak
ground acceleration, peak ground velocity) at rock sites for use in shake-
map analysis.

Introduction
In areas with high seismic-hazard levels, the correct

evaluation of  site amplification effects will allow the
production of  accurate ground-shaking maps that can
reproduce the actual pattern of  damage suffered by areas hit
by an earthquake. The most common methods to analyze
and quantify site effects are based on the shear-wave velocity
(Vs) profile, the knowledge of  which in the first 30 m (Vs30)
has an important role in classification of  the soil according
to international schemes, such as for the National
Earthquake Hazards Reduction Program [NEHRP 1997;
Eurocode 8, 2003].

In recent years different techniques have been used to
evaluate Vs profiles because knowledge of  these allows

evaluation of  the site transfer function. Many studies have
proposed the use of  the geological characteristics to assign
mean shear-wave velocity values to studied areas [Joyner and
Boore 1981]. One of  the main problems related to this
technique is that detailed knowledge of  the subsurface
geology is required. To overcome this difficulty, several
studies have proposed the use of  the average of  the shear-
wave velocities measured to a depth of  30 m (Vs30), in
agreement with the typical depth reached by a drill rig in a
single day [Joyner and Fumal 1985, Borcherdt et al. 1991].

In vertically heterogeneous media, surface waves are
dispersive; i.e. their velocity depends on the frequency. This
in turn controls their penetration depth [Aki and Richards
2002], and therefore a wider range of  frequency allows a
wider range of  investigation depth. Different studies have
been based on surface-wave dispersion to define shear-wave
velocity profiles using active seismic sources (e.g. spectral
analysis and multi-channel analysis of  surface waves)
[Nazarian et al. 1983, Park et al. 1999] and/or ambient noise
vibrations. The main advantage of  using ambient noise is in
the wider range of  frequencies available. Therefore, these
methods represent useful, non-invasive and low-cost tools to
determine the shear-wave velocities and thicknesses of  soil
layers, under the hypothesis that the noise wavefield is
dominated by surface waves.

The spatial auto-correlation (SPAC) technique
introduced by Aki [1957] is based on the assumption that
seismic noise can be viewed as the sum of  waves propagating
in a horizontal plane in different directions with different
powers, but with the same phase velocity for a given
frequency [Aki 1965]. The phase velocity can be computed
by estimating the space-correlation function for a fixed
frequency without knowledge of  the directionality of  the
waves. Then, varying the frequency, the phase velocity can be
determined as a function of  frequency.

Article history
Received March 23, 2010; accepted September 7, 2010.
Subject classification:
Seismic risk, Seismic methods, Data processing, Waves and wave analysis, Instruments and techniques.

59



The original method of  phase-velocity determination
requires only a set of  data acquired from a circular array with
one seismometer placed at the center. In urban areas, and in
a lot of  other cases, this geometry is difficult to lay out. This
led to some studies that improved on the original SPAC
method, with the definition of  the modified SPAC method
(MSPAC) [Bettig et al. 2001]. Two further modifications were
proposed by Cho et al. [2004, 2006] and Tada et al. [2006]:
the centerless circular array (CCA) and the two-radius (TR)
circular array methods, respectively.

In the present study, we follow the same approach of
Di Giulio et al. [2006], which consists of  application of
both single-station [Nakamura 1989] and multi-channel
techniques to seismic noise for complete site characterization
of  the test area of  San Fele. Moreover, we applied the non-
linear methodology implemented by Wathelet [2005] and
based on the neighborhood algorithm of  Sambridge [1999],
to invert the Rayleigh-wave dispersion curves and obtain the
S-wave velocity profiles.

The test site was chosen in the framework of  ground-
motion amplification analysis for the Campania-Lucania
region because it hosts one of  the stations of  the permanent
Irpinia Seismic Network (ISNet) [Weber et al. 2007]. Our aim
was to carefully evaluate the results obtained for the San Fele
site to determine the applicability of  the method to all of  the
other sites hosting ISNet stations. This will provide new
constraints to compute site-specific coefficients for correct
estimates of  peak ground-motion quantities (e.g. peak
ground acceleration, peak ground velocity, among others) at
rock sites [Convertito et al. 2009]. The consequent accurate
definition of  ground-shaking maps will indeed be useful for
the Civil Protection authorities to plan emergency actions in
case of  the occurrence of  earthquakes.

Geological framework
The Campania-Lucania region (southern Apennines)

has experienced numerous disastrous earthquakes that have
made it one of  the highest seismic-risk areas in Italy [Cinti et
al. 2004]. This area therefore represents a natural laboratory
to implement and test methodologies for the evaluation of
site amplification and for investigation of  the characteristics
of  seismic earthquakes.

A Quaternary–Volcanic–Tertiary–Mesozoic (QVTM)
site-condition map for the Campania-Lucania region was
proposed recently [Cantore 2008]. This map is based on the
geological units that outcrop in this area and on the Vs
profiles available from the database of  the National Strong
Motion Network (RAN). The accuracy of  this map depends
on the availability of  Vs profiles, and as stated by the authors,
the accuracy increases as more Vs profiles become available.

The structural geology of  the San Fele area is
characterized by an antiformal that involves different layers
of  the Lagonegro Unit superimposed tectonically. This Unit
is characterized by complex stratification of  siliceous
claystones, cherty limestones, and dolomites, down to a
depth of  about 5,000 m, as inferred by the San Fele well that
was drilled in the area [Improta et al. 2003, Patacca 2007].

Acquisition layout
The data analyzed in this study were collected by a group

of  researchers of  the Analisi e Monitoraggio del Rischio
Ambientale (AMRA) S.c. a r.l. society of  Naples and of  the
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio
Vesuviano (INGV department in Naples), who installed three
temporary seismic arrays to measure ambient noise in the
area of  San Fele, near the city of  Potenza (southern Italy)
(Figure 1). The instrumentation comprised four portable

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

60

Figure 1. (a) Survey area. Arrow, location of the SFL3 shelter. (b) The SFL3 ISNet station shelter. Arrow, position of the temporary seismic station (A2) used during
the noise acquisition survey. (c) The complete portable station: Lennartz MarsLite, battery and buried seismometer. (d) The LE3D 1-Hz seismometer in the hole.



61

digital Lennartz MarsLite stations equipped with 1-Hz
Le3Dlite seismometers, which were located close to the SFL3
ISNet permanent station at 15.5782 ˚E and 40.7889 ˚N. The
sampling rate and gain were set to 125 Hz and 8 nV/count,
respectively. All of  the seismometers were buried in 20-cm-
deep holes and oriented to geographic North.

The map in Figure 2 shows the location of  the area of
acquisition (red circle, upper-right corner panel). The gray
triangle corresponds to the location of  the SFL3 permanent
ISNet station. Figure 2 clearly shows the triangular geometry
of  the arrays used during the experiment. Three stations
were deployed in three concentric triangular configurations,
with rays of  about 10 m, 40 m and 80 m; the remaining
station was fixed at the center during the entire experiment.
This particular configuration was chosen to obtain the
widest azimuthal coverage; therefore, the stations were
deployed with a distance of  120˚ each from the others. The
data were collected in three times, with the timetable shown
in Table 1. Figure 3 shows an example of  the data that was
acquired for the outer array, with the seismograms acquired
for the three components shown on the left, and the relative
spectra plotted on the right. The experiment was carried out
from 27 to 28 October, 2008.

Data analysis
The ambient vibration data recorded at San Fele were

analyzed using both single-station and array techniques
implemented in the GEOPSY software (Geophysical Signal
Database for Noise Array Processing), which is distributed
in the framework of  the Site Effects Assessment using
Ambient Excitations (SESAME) project (2001–2004)
[SESAME WP05, 2002]. The surface-wave-dispersion curve
obtained has been inverted, to define a velocity profile for
the area studied.

Single station analysis (H/V ratio)
The data were analyzed using the method of  Nakamura

[1989, 2000], which was applied to the recordings from each
single station to compute the spectral ratio between the
horizontal and the vertical components (H/V ratio). This
method allows the identification of  the fundamental

resonance frequency for each site when the impedance
contrast between the surface and the deep material is high
[Bard 1998].

The importance of  H/V peak estimation in the
framework of  the S-wave velocity profile definition has
been analyzed in different studies [e.g. Bonnefoy-Claudet
2004] that have shown that the resonance frequency for a
site is close to the H/V peak and depends on the layer
thickness and the S-wave velocity. This means that the H/V
peak gives additional constraint to the S-wave velocity
profile estimation.

The results obtained through applying the H/V method
to each station of  the array are shown in Figure 4. These
results were obtained by using all of  the data recorded. The
horizontal component considered in the computation was
the squared average of  the East-West and North-South
components. To study the stability and the reliability of  the

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

Table 1. Timetable and resolution limits (kmin and kmax) for each array. Resolution limits are the minimum and the maximum resolvable wavenumbers.

Figure 2. Acquisition geometry. Crosses, locations of  the temporary
stations. Gray triangle, the SFL3 station location. The different colours
correspond to the different arrays. Dotted lines, the three triangular
configurations used. Red, inner array; green, middle array; blue, outer array.

Array name Starting time Ending time kmin - kmax

A
2008-10-28
10:59:08

2008-10-28
11:55:24

0.234 - 0.5310

B
2008-10-28
09:11:12

2008-10-28
10.48:24

0.064 - 0.1450

C
2008-10-27
14:52:56

2008-10-27
16:01:08

0.0316 - 0.0708



SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

62

Figure 3. Example of  the data acquired. Left: seismic noise records acquired for the outer array for the EW (top), NS (middle) and vertical (bottom)
components. Right: relative computed spectra for the noise acquired.

Figure 4. Mean H/V (continuous lines) and standard deviation (dashed lines) functions computed for each array. From left: the inner, middle and outer arrays.



63

results obtained, several tests have been carried out, through
changing the parameters used, as well as working on the raw
and filtered signals. This included, time windows ranging
from 5 s to 50 s, smoothing values ranging from 10 to 40, and
Butterworth band-pass filtering between 1 Hz and 15 Hz. All
of  the results obtained showed the same peaks, with little
change in the amplitudes and the widths.

Figure 4 shows the best mean H/V functions obtained
for each station using a long-term average/short-term
average (LTA/STA) antitrigger algorithm on the raw signals,
with the LTA fixed a 30 s and the STA fixed at 1 s, a time
window of  5 s, and the Konno and Ohmachi [1998]
smoothing procedure with a smoothing value fixed at 40. A
rapid analysis of  Figure 4 allows it to be stated that except for
the A0 station, there is no evidence of  a resonant frequency
for the San Fele site. Analyzing the data thoroughly for the
A0 station, i.e. for both of  the two days of  acquisition, a peak
appears at a frequency of  11 Hz. As it has been indicated from
several studies [Fabbrocino, personal communication;
Cantore 2008], this peak is due to the presence of  the shelter,
the structure hosting the permanent station (Figure 1, black
arrow). The other peaks in the plots are also of  no
significance, as they do not respect the criteria for a «clear
peak» proposed in the guidelines published in the framework
of  the SESAME project [SESAME WP12, 2004]. These peaks
are very wide (Figure 4, A1, B1, B2, C1) or their sharp
decrease is evident only on one side (B3). These features mean
that these peaks are not reliable for the resonance analysis.

Array analysis
The array data analysis was performed using the

modified MSPAC method to define the dispersion curve for
each array [Bettig et al. 2001]. The dispersion curves retrieved
were inverted to obtain the S-velocity profile for the San Fele
site. For each array, the array transfer function (ATF) was
computed, as defined as the array response for a vertically
incident impulsive signal. The ATF is commonly used to

determine the resolution of  array configurations and to
determine the dependence of  the aliasing on the azimuth, in
the wavenumber domain [Woods and Lintz 1973].

We computed the ATF for a frequency band defined
from the spectral analysis (Figure 3) and ranging from 0.5 Hz
to 20 Hz. As an example, Figure 5 shows the results obtained
for the smallest array (array A), with the results for vertical
sections of  the ATF amplitude versus wavenumber on the
left. The right side of  Figure 5 shows the resolution limits
retrieved. In particular, these curves, and those obtained for
the others two arrays, were used in the MSPAC analysis to
validate the retrieved dispersion curves. The resolution limits
are defined by the kmin and kmax values that are the minimum
and maximum wavenumbers, respectively, that can be solved
with the configuration used. The lowest wavenumber (kmin)
is defined as the value of  the main peak that reaches the ATF
magnitude of  0.5 (Figure 5, black horizontal line). The
maximum wavenumber (kmax) is defined, instead, as the
wavenumber of  the secondary peak (the closest to the
principal one) that exceeds the ATF magnitude of  0.5 [Di
Giulio et al. 2006]. Table 1 shows the values retrieved for each
of  the array configurations.

The MSPAC method computes the averages of  the
spatial auto-correlation ratio on the rings, including all of  the
possible pairs of  stations defined, starting from the location
of  each of  the stations of  the array used. The concept of  the
ring has been introduced to provide the imperfectly shaped
array (as required in the SPAC method), both in terms of  rays
and angles (r, {). Using the concept of  the rings, only the
relative locations of  the station pairs must be taken into
account in the computation of  the correlation values. In
other words, the MSPAC analysis computes the averages of
the spatial auto-correlation ratios in the plane (r, {) on the
rings with rays r1 ;r2, where r1 <r<r2.

In its general formulation, the SPAC method allows
computation of  the spatial auto-correlation ratio for each
frequency, using the following equation:

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

Figure 5. Array transfer function (ATF) for array A. Left: Azimuthal projection of  the ATF amplitude as a function of  the wavenumber k. Right: the Vs
versus frequency curves corresponding to the minimum (continuous line) and maximum (dashed line) resolvable wavelengths (0.234 and 0.5310, respectively).



(1)

where                     is the spatial auto-correlation ratio computed
using the vertical component of  the noise; J0 is the Bessel
function of  zero order; ~ is the angular frequency, and r is
the radius. Finally cR (~) is the phase velocity of  the dispersive
Rayleigh waves. Knowing                  at several frequencies,
the phase velocity profile can be estimated.

However, Equation (1) holds true if  the data have been
acquired using a perfect circular array geometry. In the case of
irregular arrays, the average of the spatial auto-correlation ratio
must be computed, so the MSPAC formulation must be used.

Using the rings concept, a given array can be divided
into several semicircular sub-arrays k, defined by a pair of
stations (i, j) having a spacing rij such that rk1 < rij < rk2. For
each sub-array, the average of  the spatial auto-correlation
ratio can be computed using:

(2)

where {ij is the azimuth between the stations (i, j). Thus,
knowing the average of  the spatial auto-correlation ratio, the
cR value of  Equation (1) can be computed for a given
frequency. In other words, knowing            , the dispersion
curves can be estimated.

Dispersion Curves
The vertical components of  the data acquired were used

to compute the dispersion curves for each array. The data
were processed using a classical procedure to synchronize all
of  the signals and to remove the mean and the trend using
SAC algorithms [Goldstein et al. 2003]. Table 1 gives the
timetable for each array configuration. To compute the
theoretical depth of  investigation for each array, we used the
Tokimatsu [1997] criteria, known as the m/3 criteria:

(3)

where kmin is the minimum wavenumber retrieved computing
the ATF function. A theoretical depth of  about 90 m was
computed for the largest aperture array (array C).

The use of  several aperture arrays allows to solve a wider
range of  frequencies. Therefore, by combining together the
results for the three arrays, we were able to solve a bandwidth
from 1 Hz to 20 Hz. Figure 6 shows the mean dispersion curves
retrieved, with the colors used to identify the arrays used. In
Figure 6, we represent together the mean dispersion curves
retrieved (in bold) and the resolution curves retrieved with the
ATF analysis for all of  the three arrays. For each of  the
dispersion curves, we also plotted the computed error bars that
give information on the reliability of  the curve. The red curve

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

64

Figure 6. Dispersion curves (continous lines) obtained from the correlation coefficients for the three arrays. Vertical bars, errors computed for each dispersion
curve. Dashed lines, minimum and maximum resolvable wavelengths retrieved for each array. Blue, outer array; green, middle array; red, inner array.

, rr J
c0z R

=t ~
~

~^ ^h hc m
,rzt ~^ h

,rzt ~^ h

1 , ,r
< <

k ij ij
r r r

ij
1 2k ij k

=t ~ r t { ~ {D^ ^h h/

ijkt ~^ h

3 3
2d kmax

max

min
= =
m r



65

in Figure 6 appears to be better constrained, which is due to
the high-density solution retrieved in the frequency–velocity
grid obtained after seeking all of  the possible solutions for the
data [Wathelet et al. 2005]. Starting from these three curves,
we computed a single mean dispersion curve that was used to
obtain the S-wave velocity profile.

Inversion
The mean dispersion curve computed for the data

acquired was inverted to obtain the S-wave velocity profile
using the implementation of  the neighborhood algorithm
[Sambridge 1999] performed by Wathelet et al. [2004]. This
implementation allows the generation of  random velocity
models, which are then used to compute the theoretical
dispersion curves. To find the best velocity model, the
theoretical dispersion curves are compared to the
experimental ones, and the misfit value is computed. The
solution of  the inversion, i.e. the final velocity model, is that
which has the lower misfit value.

As in all classical inversion problems, we had to define
the data to invert and the parameters to retrieve. In our case,
the data were represented by the mean dispersion curve
computed for the vertical component of  the ambient noise.
The parameters were, instead, the S-wave velocity and the
thickness of  the soil layer. To explore all of  the parameter
space of  the velocity models and to choose the best one, we
used a trial-and-error procedure. As the geological model
known for the area, which comes from well data, is available
only for the P-wave, we cannot use this as the starting model
in our procedure. Nonetheless, we can use information
about the structure and density values.

Using this little information, we assumed a velocity
model as a two-layered structure: a sedimentary layer over a
bedrock. By studying the best-fit model retrieved, as the
inversion proceeded, we were able to modify our parameters
(S-wave velocity and thickness range). Starting from this
simple two-layered model, we moved towards a more
complicated sedimentary layer, increasing the number of  its
sub-layers (Table 2). Finally, the best model retrieved had 1,000

sub-layers in the sedimentary layer over a bedrock. This high
number of  sub-layers allows us to assume a gradient feature
for the sedimentary layer, in agreement with the P-wave
velocity profile known for the area. To test the robustness of
the final model retrieved, and to randomly explore all of  the
parameter space, we ran 3 to 4 inversion processes for each set
of  parameters (Table 2) using different seeding (i.e. starting
model) and exploring about 7,000 models for each run.

Figure 7 shows the one-dimensional S-wave velocity
model obtained, and the corresponding dispersion curves.
The Vs model shows a velocity gradient for the sedimentary
layer ranging from 200 m/s to 800 m/s down to about 50 m
in depth. Each model explored in Figure 7 is plotted using a
color that corresponds to the computed misfit value. All of
the models approach the best-fit model (Figure 7, red). This
agreement is evident down to 40 m, while instead a broader
convergence appears for the bedrock velocity. The variability
in the range of  a 40-50 m depth does not have great
relevance. The misfit values computed for the profiles
plotted in pink in Figure 7 are the highest that were retrieved
in the entire inversion process, and this means that these
models are those that are badly constrained.

The fundamental Rayleigh-wave dispersion curves
computed for all of  the models retrieved are also plotted in
Figure 7, and they are compared with the experimental
model (Figure 7, black solid line). Figure 7 also shows the
convergence of  the velocity profiles. All of  the theoretical
dispersion curves fit the uncertainty of  the mean dispersion
curve very well, as indicated by the error bars, which
confirms the robustness of  the result obtained. The
amplitude of  the error bars is larger at low frequency, which
corresponds to greater depth of  exploration. This can be
related to the increasing uncertainty of  the phase-velocity
estimate at lower frequency, and to the decrease in the
sensitivity with depth.

Conclusions
In this study, we have shown the results of  the seismic

noise analysis performed on data acquired at the San Fele site

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

of  sub-layer Min Max Vmin Vmax Vmin Vmax Vmin Vmax

1 100 150 3500 1000 4500

1 1000 50 2500 1000 4500

2 1 500 50 500 150 1000 1000

4 1 100 50 350 300 800 1000 3000

10 1 100 50 1000 500 2000 500 2000

1000 1 90 50 500 150 1500 50 3500

Number Thickness (m) Top (m/s) Bottom (m/s)

Sedimentary layer Bedrock

Table 2. Velocity and thickness parameters used in the inversion procedure.



(southern Apennines). The aim of  this study was to
investigate the feasibility of  the MSPAC methodology to
obtain S-wave velocity profiles from ambient noise vibration
data also in very complex areas, such as the southern
Apennines. We used both single-station and array data
analysis, obtaining results that are in agreement.

The single station analysis, which were performed
using the H/V methodology, show that no resonance
frequency characterizes the San Fele site. This might mean
that there is no high-velocity contrast between the soil-layer
and the bedrock at the site. This result is confirmed by that
obtained using the MSPAC methodology. Indeed, Figure 7
shows that a gradient S-wave velocity profile is obtained
down to about 50 m, and that there is no abrupt change in
the velocity of  the underlying layer.

Furthermore, the procedure used has allowed
simulation of  quite a complex model in the sedimentary
layer, i.e. a velocity gradient, using a simple two-layered
model. We assume that the gradient velocity profile
retrieved is probably due to the presence of  a single tectonic
unit, for which the velocity variation gives information as
to the different degrees of  alteration and/or different sizes
of  the grain. This is supported by the data from the San Fele
well, which was drilled down to a depth of  about 5,000 m,
and encountered a pile of  imbricate deposits that are
referable to the Lagonegro unit [Patacca 2007, Improta et

al. 2003]. The data available for this well show that down to
about 40 m in depth the lithology is referred to as marly
shale deposits that overlap shale and siliceous shale deposits.
These data appear to explain the broader convergence of
the S-wave velocity profile that is obtained at the depth of
40-50 m (Figure 7). With reference to the NEHRP
classification, the area of  San Fele can be classified in the C
class, as dense soil and unconsolidated rocks due to the high
number of  fractures.

The present study shows once again that the combined
use of  H/V and MSPAC methodologies provides robustness
for the results obtained. Moreover, the use of  ambient-
noise vibration data is a very useful, non-invasive and
low-cost method to obtain shear-wave velocity profiles and
thicknesses of  soil-layers.

Finally, the Vs profile is one of  the most important
pieces of  information for the assessing of  site effects in
earthquake engineering. For this reason, we believe that the
methodology applied to the San Fele site is very useful in the
seismic-risk assessment for the southern Apennines.

Acknowledgements. This study was supported financially by
AMRA S.c. a r.l. (www.amra.unina.it) in the framework of  the SAFER-SE-
RISK project (Sixth Framework Programme for Sustainable Development,
Global Change and Ecosystem Priority, 6.3.IV.2.1: Reduction of  seismic
risks for specific targeted research or innovation projects; contract no.
036935). Dr. Danilo Galluzzo is greatly acknowledged for his contributions
to the data acquisition.

SITE EFFECT CONSTRAINTS FROM SEISMIC NOISE

66

Figure 7. S-wave velocity profiles (left) and theoretical dispersion curves (right).



67

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*Corresponding author: Margherita Corciulo,
Laboratoire de Géophysique Interne et Tectonophysique (LGIT),
Université J. Fourier, Grenoble, France;
e-mail: margherita.corciulo@obs.ujf-grenoble.fr.

© 2010 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights
reserved.

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