surface wave dispersion measurements from ambient seismic ... · surface wave dispersion...

Geophys. J. Int. (2010) 180, 1242–1252 doi: 10.1111/j.1365-246X.2009.04476.x

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Surface wave dispersion measurements from ambient seismic noiseanalysis in Italy

Hongyi Li,1,2 Fabrizio Bernardi3 and Alberto Michelini31Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education, China. E-mail: [email protected] of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China3Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata, 605, Rome 00143, Italy

Accepted 2009 December 7. Received 2009 September 22; in original form 2008 October 25

S U M M A R YWe present the surface wave dispersion results of the application of the ambient noise methodto broad-band data recorded at 114 stations from the Istituto Nazionale di Geofisica e Vul-canologia (INGV) national broad-band network, some stations of the Mediterranean VeryBroadband Seismographic Network (MedNet) and of the Austrian Central Institute for Me-teorology and Geodynamics (ZAMG). Vertical-component ambient noise data from 2005October to 2007 March have been cross-correlated for station-pairs to estimate fundamentalmode Rayleigh wave Green’s functions. Cross-correlations are calculated in 1-hr segments,stacked over periods varying between 3 months and 1.5 yr. Rayleigh wave group dispersioncurves at periods from 8 to 44 s were determined using the multiple-filter analysis technique.The study region was divided into a 0.2◦ × 0.2◦ grid to invert for group velocity distribu-tions. Checkerboard tests were first carried out, and the lateral resolution was estimated to beabout 0.6◦. The resulting group velocity maps from 8 to 36 s show the significant differenceof the crustal structure and good correlations with known geological and tectonic featuresin the study region. The Po Plain and the Southern Alps evidence lower group veloci-ties due to soft alluvial deposits, and thick terrigenous sediments. Our results also clearlyshowed that the Tyrrhenian Sea is characterized with much higher velocities below 8 km thanthe Italian peninsula and the Adriatic Sea which indicates a thin oceanic crust beneath theTyrrhenian Sea.

Key words: Tomography; Surface waves and free oscillations; Crustal structure.

1 I N T RO D U C T I O N

Seismic studies of the velocity structure of the Earth have beentraditionally based on observations of seismic waves emitted byearthquakes or explosions. Based on those ballistic waves, manyresults about the Earth’s structure have been obtained by measuringthe traveltimes of body waves, dispersion curves of the surfacewaves and waveform modelling. In Italy and surrounding areas,many traditional surface wave studies based on earthquake datahave been made (e.g. Panza et al. 1980; Mantovani et al. 1985;Pontevivo & Panza 2002; Chimera et al. 2003; de Lorenzo et al.2003; Marone et al. 2004; Raykova et al. 2006).

Surface waves at different periods are sensitive to the Earth struc-ture at different depths. In general, short periods tend to sample theslow layers closer to the surface, whereas the long periods are sen-sitive to faster velocities found deeper in the Earth. Thus, surfacewave dispersion measurements have been widely used to study thestructure of the Earth’s crust and upper mantle.

Measurements made from earthquake-based surface waves, how-ever, have several limitations: (1) inadequate path coverage due to

the inhomogeneous distribution of earthquakes; (2) strong depen-dency on the determination of origin time and source parametersand (3) the difficulties in making short-to-intermediate period dis-persion measurements from teleseismic events since high frequencyinformation is usually lost due to intrinsic attenuation and scatteringalong ray paths.

Recently, it has been shown that a random wavefield generatedby scatters in the near-surface structure of the Earth, includes infor-mation on the Green’s function of a wave travelling along the pathbetween two receivers. This information can be extracted by com-puting the cross-correlation between records at two receivers oversufficiently long times (Weaver & Lobkis 2001a,b, 2004; Derodeet al. 2003; Snieder 2004; Wapenaar 2004; Larose et al. 2005).In seismology, two types of random wavefields have been used toretrieve Green’s function by cross-correlating coda waves and suf-ficiently long ambient noise recordings. Seismic coda waves areproduced by multiple scattering of seismic waves from small-scaleheterogeneities in the lithosphere (Sato & Fehler 1998). By stack-ing cross-correlation functions of coda waves in Mexico, Campilloand Paul (2003) extracted the Rayleigh and Love waves Green’s

1242 C© 2010 The Authors

Journal compilation C© 2010 RAS

Geophysical Journal International

Ambient seismic noise analysis in Italy 1243

Figure 1. Distribution of seismic stations in Italy and adjacent regions used in this study, denoted by triangles. The thick straight lines are the section lines ofthe vertical profiles (AA′, BB′ and CC′) shown in Fig. 10.

functions. Ambient seismic noise is excited by randomly distributednatural and artificial sources such as sea waves, atmospheric pertur-bations, traffic and human activities. Shapiro and Campillo (2004)and Sabra et al. (2005a) have confirmed that surface wave Green’sfunctions can be extracted by cross-correlating long time-series ofseismic ambient noise. Extensive work has been performed usingthe ambient noise method in the United States and the Pacific North-west (Sabra et al. 2005b; Shapiro et al. 2005; Bensen et al. 2008;Liang & Langston 2008), in South Korea (Kang & Shin 2002; Choet al. 2007), in China (Yao et al. 2006, 2008; Li et al. 2009), inNew Zealand(Lin et al. 2007) and in Europe (Yang et al. 2007;Stehly et al. 2009). In general, ambient noise method provides sur-face wave dispersion measurements at shorter periods with higherresolution than traditional methods under the conditions of greatlydensifying seismic networks and uniform noise field distribution.

The Italian peninsula features a complex tectonic setting whichresults in strong lateral heterogeneities of the seismic velocity struc-ture (Panza et al. 1980; Mantovani et al. 1985; Pontevivo & Panza2002; Li et al. 2007). In recent years, high quality, continuousbroad-band recordings from the recently installed INGV national

broad-band network, the MedNet network and the ZAMG network(Fig. 1) have become available. These networks provide an unprece-dented dense station coverage to perform seismological analysis inItaly.

The main purpose of this study is to apply the ambient seismicnoise technique to Italy to obtain high-resolution Rayleigh wave,fundamental mode, dispersion curves. Vertical-component time-series recorded at 114 broad-band stations in Italy and adjacentregions between 2005 October and 2007 March are cross-correlatedto yield estimated Rayleigh wave Green’s functions. The resultinggroup velocity maps from 8 to 36 s can provide important indicationson the crust and the upper mantle of Italy and surrounding regions.

2 DATA P RO C E S S I N G A N D G RO U PV E L O C I T Y M E A S U R E M E N T S

We collected continuous broad-band vertical-component seismicdata recorded by 114 stations from the INGV national network(Amato & Mele 2008), MedNet and ZAMG from 2005 October

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1244 H. Li, F. Bernardi and A. Michelini

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Figure 2. Monthly cross-correlations computed for five station-pairs (shown on the upper left-hand panel) for frequency band 0.05-0.2 Hz: ARSA-TUE,DOI-SACS, CING-GROG, CING-AMUR and ESLN-CIGN. The stacks over all the available months are displayed at the top of each panel.

to 2007 March. In a manner similar to the standard, noise cross-correlation processing procedure (Shapiro and Campillo 2004;Sabra et al. 2005a; Bensen et al. 2007) the data were (i) windowedin one-hour length time series, (ii) removed trend and mean value,(iii) high pass filtered at 0.01 Hz, (iv) decimated to 1 sample persecond while preventing aliasing and (v) whitened.

The processed 1-hr-long traces were cross-correlated between allstation-pairs and then stacked all the available cross-correlationsfor each station-pair into a single time-series. Attention has beenmade to maintain a minimum distance of three wavelengths foreach station-pair when calculating the cross-correlation. The num-ber of cross-correlations in the stack varies between 3 months and1.5 yr depending on station availability. Theoretically, the resultingcross-correlations should show the same velocity and dispersionfeatures on both positive and negative lags since waves travelling inopposite directions along the path between a station-pair sample thesame structure. However, due to not perfectly isotropic noise fielddistribution, asymmetric cross-correlations are often observed.

In this study, we first investigated the dependence of the cross-correlations on the noise source distribution. Fig. 2 shows monthlycross-correlations for five station-pairs over the frequency band

0.05–0.2 Hz. For the station-pair ARSA-TUE, the amplitude of thepositive correlation lag representing waves travelling from ARSAto TUE is much smaller than the negative lag. One-sided cross-correlations are also clearly observed for other four station pairsin Fig. 2, the correlation lags showing large amplitudes representwaves propagating from DOI to SACS, GROG to CING, CINGto AMUR and CIGN to ESLN, respectively. Previous studies haveshown that the noise recorded in Europe originates mainly fromthe northern Atlantic Ocean, and the Mediterranean Sea is also animportant noise source region (Friedrich et al. 1998; Marzorati &Bindi 2006; Pedersen et al. 2007; Marzorati & Bindi 2008), whichresult in one-sided asymmetric cross-correlations. We also noticedthat the cross-correlations computed in winter show a higher signal-to-noise ratio (SNR) than those in summer. Although the cross-correlations are asymmetric and display seasonal variations due tonon-uniformal noise distribution, the cross-correlations generallyshow coherent waveforms and contemporaneous arrivals of surfacewaves from month to month.

Fig. 3 gives monthly cross-correlations over the frequency band0.02–0.05 Hz. Compared with the Fig. 2, on most of station-pairsexcept for CING-AMUR, we observed clear signals simultaneously

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Figure 3. Monthly cross-correlations computed for five station-pairs for frequency band 0.02-0.05 Hz: ARSA-TUE, DOI-SACS, CING-GROG, CING-AMURand ESLN-CIGN. The stacks over all the available months are displayed at the top of each panel.

arriving on both positive and negative correlation lags which indi-cated that the directionality of the noise source is weak and thenoise field is close to diffusive in this frequency, similar noise be-haviours have been also reported by Pedersen et al. (2007) in theBaltic shield.

In this study, in order to simplify data analysis and enhance theSNR, we averaged the positive and negative correlation lags to createa symmetric cross-correlation, and more than 3000 station-pairshave been discarded by selecting only cross-correlations with SNRlarger than six to ensure correctly identified arrival-time of surfacewaves. Fig. 4 gives an example of a cross-correlation record sectionwith respect to station CING. This figure shows that a variety ofazimuths produce clear signals with physically reasonable moveouts(∼2.7 km s−1).

The group velocity dispersion measurements were made using themultiple-filter technique (Dziewonski et al. 1969; Herrmann 1973).The waveforms were narrow bandpass filtered with the operator exp[− α(ω − ω0)2/ω2

0], where ω0 is the center frequency. Since thereis a trade-off between resolution in the time and frequency domainswith such filtering, the tunable parameter α is used to best balance

resolution depending on interstation distance (Levshin et al. 1989).We tested the α values for various distance ranges using syntheticseismograms. We found α = 6.25, 12.5 and 25 for distances between0–100, 100–250 and 250–1000 km, respectively.

Fig. 5 shows group velocity dispersion curves measured fromsix station-pairs along paths through distinct geological regions. Asseen in Fig. 5, dispersion curves measured from station-pairs TRI-BOB generally showed lower values than those from DOI-SACS,MCEL-MAON and SERS-OSKI.

In general, Rayleigh waves sample down to depths approximatelyone-third their wavelength. Low group velocities at short periods(<15 s) usually are associated with sedimentary layers in the shal-low part of the crust. Note that the path TRI-BOB is through thePo Plain, a region considered to have very thick terrigenous sedi-ments (Scarascia & Cassinis 1997; Waldhauser et al. 1998, 2002;Kummerow et al. 2004; Tesauro et al. 2008). At longer periods,Rayleigh waves sample predominantly the upper mantle. The dis-persion curve between stations SERS and OSKI shows well the fastgroup velocities occurring at periods between 15 and 25 s whencompared to the other paths.

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Figure 4. A cross-correlation record section centred at the station CING which is bandpass filtered from 8 to 50 s. Traveltime–distance plot of cross-correlatedtraces are shown on the right-hand panel with the corresponding paths delineated by black lines on the left-hand panel. The grey line indicates the approximatearrival times for Rayleigh waves in this band.

3 I N V E R S I O N F O R G RO U PV E L O C I T Y M A P S

By applying the data selection criteria mentioned in previous sec-tion, the Rayleigh wave dispersion measurements from more than2800 paths were used to invert for group velocity maps at periodsfrom 8 to 36 s. The study region was divided into a 0.2◦ × 0.2◦ grid,and velocities between grid nodes were calculated with bilinear in-terpolation. The Occam’s inversion scheme was adopted to invertfor group velocity distributions. The Occam’s inversion method pro-posed by Constable et al. (1987) and deGroot-Hedlin & Constable(1990) seeks a smooth model that satisfies the observations whileallowing for a fine grid for model discretization. Here, we just givea brief description of the method. The velocity model is representedas m with N unknowns and the observation data of M paths as d.The observational errors are stored in a diagonal matrix w, andthe smoothness of the model is denoted by roughness R definedas

R = Rx + Ry = ||δx m||2 + ||δym||2, (1)

where δx m and δy m are vectors composed of first-order spatial par-tial derivatives in x and y directions, respectively. In our inversion,the objective is to minimize the following function:

||wd − wF(m)||2 + μ(||δx m||2 + ||δym||2). (2)

The first term describes the fitting to observational data, the secondterm represents the smoothness constraint, and μ is a smoothingfactor. The selection of the smooth parameter μ is, however, some-what subjective. As the value of μ is increased, the resulting modelbecomes smoother because a larger amount of model conditioningis applied. We performed a series of tests with different μ to deter-mine its optimal value by considering the trade-off curve betweendata misfit and model roughness as shown in Fig. 6.

Optimal results were obtained when the decrease of residualstarted to slow down at decreasing μ. Thus the final model is theresult of a compromise between achieving more details and main-taining a reasonable smoothness for the velocity model. From Fig. 6,we found that the misfit began to decrease gently as μ is smallerthan 2, and the decrease of roughness became to slow down when μ

is larger than 0.5. The value of μ between 0.5 and 2 could providereasonable model resolution, and we selected μ = 1 in the presentinversion. A more detailed discussion of this method is given byConstable et al. (1987) and deGroot-Hedlin & Constable (1990),and a recent application can be found in Huang et al. (2003) and Liet al. (2009).

The resolution of surface wave tomography depends primarilyon path coverage and the azimuthal distribution of paths. Fig. 7shows the number of dispersion measurements at different periodsand the path coverage at 15 and 30 s. The path coverage is best forperiods 10–26 s with over 2000 measurements. Coverages at shorteror longer periods become sparse, but the spatial pattern of coverageremains similar. As it can be seen in Fig. 7, the overall coveragepattern is reasonably good, however, the path distribution is noteven in the study region. Owing to the few available stations on theeastern side of the Adriatic Sea, the azimuthal distribution of pathsis generally poor east of peninsular Italy. In general, path densityis highest within the Italian peninsula and it decreases graduallytowards the perimeter of the study area.

Checkerboard tests were performed to estimate resolution.Fig. 8(a) shows a theoretical velocity model that is discretized intoa 0.2◦ × 0.2◦ grid as the real model used in our inversion. The sizeof alternatively high- and low-velocity cells is 0.6◦. Each cell hasa constant velocity 5 per cent above or below an average velocityof 2.7 km s−1. Synthetic data of group velocities were calculatedaccording to the actual paths at 15 and 30 s period. Finally, thedata were used to reconstruct the velocity model. The resulting

C© 2010 The Authors, GJI, 180, 1242–1252



Figure 5. Top panel: Interstation paths for group velocity measurements:SERS-OSKI, DOI-SACS, MCEL-MAON, CAFE-SBPO, AMUR-MABIand TRI-BOB. Bottom panel: group velocity curves measured from thepaths shown on the top.

velocity models are shown in Figs 8(b) and (c) where we observethat the resolving power is generally good in the Italian territoryand Tyrrhenian Sea but it deteriorates towards the periphery. Pathcoverages at 30 s period become worse, but the spatial coveragepatterns generally remain similar.

Fig. 9 presents the results of group velocity tomography at period8, 15, 24 and 30 s. At 8 s period, the lowermost wave velocitiesare found beneath the Po Plain. At 15 s period, the Po Plain, thenortheastern side of the Apennines and Sicily are characterized bylow wave velocities. Conversely, the fastest velocities occur beneaththe Tyrrhenian Sea. At 24 and 30 s period, the distribution of lowvelocities beneath the Po Plain widens to the Central and EasternAlps and in the Apennines. Also, much higher velocities occur

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beneath the Tyrrhenian Sea whereas in Sicily the pattern of highand low velocities reverses with higher velocities resolved.

4 D I S C U S S I O N

4.1 Interpretation of the group velocity maps

Surface waves at different periods are sensitive to the Earth struc-ture over different depth ranges. It is generally true that surfacewaves of short period tend to sample materials close to the sur-face, and long period surface waves are more affected by deepstructures in the Earth, although short period surface waves have abetter depth sensitivity in the shallow crust and long periods havea relatively poor depth resolution at deep depth since surface waveenergies tend to concentrate near the surface. Thus, at short periods(8–20 s), the group velocities are much influenced by the upper partof the crust. Short-period low-velocity anomalies are usually a goodindicator of sedimentary basins since sediments feature low seismicvelocities.

As seen in Fig. 9, low group velocities at short periods are ob-served beneath the Po Plain and the Apennines. These low velocitieslikely reflect the thick alluvial basin of the Po river and the sedi-mentary deposits of the Apenninic and Alpine foredeeps and theextensional basins distributed along the external and internal frontsof the Apennines (Waldhauser et al. 1998, 2002; Kummerow et al.2004; Di Stefano 2005). In the period range between 8 and 20 s theRayleigh wave energy is mostly concentrated in the crust in the landareas, but it likely samples the higher velocities of the upper mantlein oceanic areas where the crust is relatively thin. This is most likelythe reason why high group velocities occur in the Tyrrhenian Seaarea where seismic sounding data have long revealed a very thinoceanic crust (Duschenes et al. 1986; Geiss 1987; de Voogd et al.1992; Pepe et al. 2000).

The overall pattern of velocity distribution beneath the Apenninesand southern Alps is compatible with the previous group velocitystudies that used earthquake data (Pontevivo & Panza 2002). Thenorthern and central Apennines and southern Alps are clearly char-acterized with low velocities. P-wave tomographic results from (DiStefano et al. 1999) also revealed low-velocity anomalies beneaththe northern Apennines and high-velocity anomalies beneath the

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Figure 7. Left-hand panel: distribution of dispersion measurements at different periods. Middle panel: path density at 15 s. Path density is defined as thenumber of rays intersecting a 0.2◦ × 0.2◦ cell. Right-hand panel: path density at 30 s.

Figure 8. Checkerboard resolution tests with a grid spacing of 0.6◦. (a) theoretical model for T = 15 s with 5 per cent velocity disturbance; (b) inversionresult at T = 15 s path density and (c) inversion result at T = 30 s path density.

southern Apennines and the Calabrian arc; at 22 km depth, a con-tinuous low-velocity zone is observed beneath the whole Apenninicbelt, and the width of the low-velocity zone gets narrower in thesouthern Apennines than in the northern Apennines. Studies fromMele et al. (1996, 1997) also observed evidence for a broad low-velocity and high-attenuation anomaly of Pn and Sn phases beneaththe Apenninic area. However, some inconsistencies also exist. Low-velocity anomalies from P-wave tomography are imaged beneaththe western Sicily and the Aeolian volcanoes between 8 and 22 km,which are interpreted to be crustal magmatic bodies, probably con-nected with thermal anomalies at depth; high-velocity anomaliesare observed beneath the eastern Sicily (the Mt Etna volcano) be-tween 8 and 22 km, and they suggested that is probably associatedwith the presence of intrusive bodies and the absence of a widemagma chamber in the crust (Di Stefano et al. 1999). Moreover,local seismic tomography studies underneath the Mt Etna volcano

from Villasenor et al. (1998) found that a high-velocity anomaly islocated in the depth range 2–12 km beneath the southeastern flankof the volcano and a low-velocity-zone extends from beneath theCrater region to a depth of 10 km, however, significantly differenttomographic results are also proposed for the same area, for exam-ple, Aloisi et al. (2002) revealed that a major high-velocity-bodyis recognized in the upper 10 km beneath the Crater area and thesoutheastern flank of the volcano, and low velocities are found tothe west and the east of the high-velocity-body and also the south-western flank of the volcano. In our study, most of the Sicily standsout as having a low velocity at 15 s. Since the accuracy of earth-quake location and arrival-time picking, as well as data coverage, allinfluence the final tomographic results based on earthquake data, es-pecially at shallow depths, in order to address above controversies,further studies with a finer parametrization and higher resolutionare needed in the future. At this period, there is also a new feature

C© 2010 The Authors, GJI, 180, 1242–1252



Figure 9. Estimated Rayleigh wave group velocity maps at periods of 8, 15, 24 and 30 s. Period is also indicated in the lower right-hand corner of each map.

recovered from our ambient noise tomography is—the velocity onthe Tyrrhenian side of the northern Apennines which is much higherthan that on the Adriatic side. This relevant contrast is well visibleat periods 24 and 30 s.

In the period range between 20 and 35 s, Rayleigh waves areprimarily sensitive to the thickness of crust and the shear velocitiesin the lower crust and uppermost mantle. The group velocities canreflect the Moho depth variation which correlates inversely with

group velocities—high velocities in regions with a thin crust andlow velocities in regions with a thick crust.

In Fig. 9, the velocity maps at 24 and 30 s still exhibit low ve-locities associated with the Alps and northern-central Apennines.The low velocities beneath the central Alps are probably related tothe thick crust in this region, where the crust has been estimated tobe about 50 km (Waldhauser et al. 2002; Kummerow et al. 2004;Li et al. 2007; Tesauro et al. 2008). The low-velocity anomalies

C© 2010 The Authors, GJI, 180, 1242–1252



beneath the northern-central Apennines are also reported by pre-vious work using earthquake data (e.g. Mele et al. 1996, 1997; DiStefano et al. 1999; Pontevivo & Panza 2002). Deep crustal roots,beneath mountain regions resulting from isostatic compensation,can explain the low velocities in the northern-central Apennines.The high velocities are observed underneath Sicily which is differ-ent from Di Stefano et al. (1999) and Lucente et al. (1999)’s resultsfeatured with low-velocity anomalies at 38 km depth. However,attenuation studies from Mele et al. (1996, 1997) did not show ahigh-attenuation anomaly of Pn and Sn phases beneath the Sicily,which is usually associated with a low-velocity anomaly. Since thepath density and the resolution of the Sicily island become muchpoorer at longer period as shown in Figs 7 and 8, it is hard to makefurther analysis based on our results.

We also note that the low-velocity feature beneath the Apenninesextends as far south as 41◦ latitude approximately. In the southernpart of the Apennines relatively higher velocities occur. The veloc-ity contrast between the northern and southern Apennines has beenalso observed by other authors (Di Stefano et al. 1999; Pontevivo &Panza 2002). This transition corresponds to a well-known bound-ary between the northern and southern Apenninic arcs (Patacca &Scandone 1989).

4.2 Shear wave velocity structures

From the group velocities obtained in the previous section, weextracted the pure path dispersion curves for each grid node. Al-though the sensitivity kernel of group velocity has a complicatedrelation with the structure, in general, longer period Rayleigh wavesare more affected by deeper structures, therefore, these data mayprovide valuable insights into the lateral variation of shear wavevelocities in different depth ranges.

Inversions for the shear wave velocity structure at each node ofthe 0.2◦ × 0.2◦ grid was carried out in this study with a programdeveloped by Herrmann & Ammon (2004) using linear steps. TheS-wave velocity in each layer is taken as the inversion parameter, andP-wave velocity and density are calculated from S-wave velocitywith empirical formulas. The same initial model is used for allnodes in our inversion, then the inversion was repeated 15 timeswith 15 different initial models by referring to recent tomographyresults (Di Stefano 2005; Li et al. 2007), and the final S-wavevelocity structure was averaged from the 15 inversion solutions.As we know, shear wave structure inversion based on surface wavedispersion data suffers from poor resolution and non-uniquenessdue to strong non-linearity, therefore, we did not put much effortinto seeking the true model’s possible ranges in this study. Ourgoal is to obtain a preliminary 3-D structure, which could satisfyour group velocity dispersion data and show the essential crustaland upper-mantle features of different tectonic units in the studyarea.

Fig. 10 shows shear wave velocity structure along three verti-cal profiles from the surface down to 40 km depth. As seen in theprofile AA

′, there are obvious differences in the structures along

the profile. It is remarkable that shear wave velocities beneath thenorthern Tyrrhenian Sea clearly display much higher values thanthose beneath the northern Apennines and the northern AdriaticSea at depth deeper than 8 km, which are consistent with the shal-low Moho depth beneath the Tyrrhenian Sea area where seismicsounding data have revealed a very thin oceanic crust (Duscheneset al. 1986; Geiss 1987; de Voogd et al. 1992; Pepe et al. 2000), andnormal continental crust beneath the Apennines and the Adriatic

Figure 10. Vertical cross-sections of shear wave velocity along the linesAA′, BB′ and CC′ in Fig. 1. Topography is plotted above each profile (blackarea).

Sea. In the profile BB′

crossing Sicily to the southern Apennines,the lateral variation of velocities is still evident. The Sicily islandshows lower velocities at depth 10–15 km than the southern Apen-nines, however, at depth larger than 20 km, the pattern is reversedand the Sicily island is characterized with high velocities. Althoughsome geophysical studies have suggested that the local low-velocityanomaly in this region is associated with crustal magmatic bodies,more detailed studies are needed to be performed in the future.

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Another obvious feature in the profile BB′

is that at depth deeperthan 15 km, the southern Tyrrhenian sea region features with highvelocities which reflect its thin oceanic crust. Along the profile CC

′,

the lowest velocities at shallow depth occurred beneath the Po Plainwhich is associated with thick cover of alluvial deposits in thisarea.

5 C O N C LU S I O N S

In this study, we have obtained group velocity maps of Rayleighwaves between 8 and 36 s, using ambient noise data recorded at114 broad-band seismic stations from INGV, MedNet and ZAMG.Cross-correlations are computed in one-hour segments with a totalduration ranging between 3 months and 1.5 yr. Group velocitydispersion curves at periods from 8 to 44 s were determined usingthe multiple-filter analysis technique. The lateral resolution in theItalian peninsula and Tyrrhenian Sea is estimated to be ∼0.6◦, buta poor resolution is found on the northeastern side of the Italianpeninsula because of the few available stations.

Our group velocity maps show that at increasing period, thevelocity distribution pattern changes systematically and correlateswell with the known geological and tectonic features—sedimentarybasins and lateral variation of crustal thickness. Compared withprevious surface wave studies which have relied on traditionalearthquake-based measurements, this study has a much denser datacoverage and higher resolution. In general, previously found fea-tures are confirmed and, in addition, our results showed that theTyrrhenian Sea is clearly featured with much higher velocities be-low 8 km due to its thin oceanic crust.

A C K N OW L E D G M E N T S

We are grateful to the Centro Nazionale Terremoti (CNT) of INGV,MedNet and ZAMG data center for providing the data. We thankZhongxian Huang for offering the inversion code. We benefited fromdiscussions with R. B. Herrmann and Lupei Zhu. This research wassupported in part by the 2004–2006 DPC-S4, 2007–2009 DPC-S3projects and the open fund (No. GDL0708).

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