Engineering Geology 182 (2014) 182–200

Contents lists available at ScienceDirect

Engineering Geology

j ourna l homepage: www.e lsev ie r .com/ locate /enggeo

What we can learn about slope response to earthquakes from ambientnoise analysis: An overview

Vincenzo Del Gaudio a,⁎, Sandro Muscillo a, Janusz Wasowski b

a Dipartimento di Scienze della Terra e Geoambientali, Università degli Studi di Bari “Aldo Moro”, Italyb Istituto di Ricerca per la Protezione Idrogeologica, Consiglio Nazionale delle Ricerche, Bari, Italy

⁎ Corresponding author.E-mail address: vincenzo.delgaudio@uniba.it (V. Del G

http://dx.doi.org/10.1016/j.enggeo.2014.05.0100013-7952/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 17 May 2014Available online 29 May 2014

Keywords:LandslidesEarthquakesSite amplificationAmbient noiseNakamura's methodCross-correlation analysis

Earthquake induced slope failures are responsible for a significant amount of life loss and damage, and theireffective mitigation requires further advancements in our comprehension of slope behaviour under seismicshaking. One source of uncertainty in seismic landslide susceptibility assessment is the phenomenon of enhancedamplification of ground motion along down slope directions. This implies a strength demand beyond that esti-mated by standard slope stability analysis. An extensive accelerometer monitoring of slope dynamic responsein areas exposed to seismic landslide hazard is unfeasible. An alternative approach can take advantage of recentdevelopment of reconnaissance techniques based on the analysis of ambient noise recorded by portable instru-ments. Themost popular technique, known as Nakamura or HVNRmethod, consists in analysing H/V spectral ra-tios between Horizontal and Vertical components of Noise Recording, and allows the recognition of siteresonance frequencies. The application of HVNR to complex site conditions typical of marginally stable slopesis often difficult and requires the development of “ad hoc” procedures both for acquisition and analysis ofnoise recording. Tests in different geologic and geomorphic settings show that an analysis of azimuthal variationof spectral ratios can reveal the presence and orientation of directional resonance, whereas the recognition ofmain resonance frequencies requires a proper selection of signals to be analysed. Efforts to evaluate amplificationfactors currently rely on numerical simulations, which in turn require S-wave velocity of slope materials. Ambi-ent noise analysis in terms of velocitymodels can contribute through the inversion of H/V spectral ratios and sur-face wave velocity dispersion curves derived from the processing of multiple simultaneous noise recordings.However these applications require a correct identification of the nature of surface waves present in the noiserecording.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

In the strong earthquake scenario, widespread slope failures repre-sent not only a potential source of life loss and costly damages, butalso a threat to road and lifeline networks essential for an effectiveemergencymanagement. Seismically triggered landslides can cause ad-ditional collateral hazards, e.g. disastrous flooding resulting from riverdamming. For example, the moment magnitude (Mw) 7.9 Wenchuanearthquake of 12 May 2008 induced over 60,000 landslides (Gorumet al., 2011); these were directly responsible for about 20,000 victims,caused extensive damages to irrigation channels, and interrupted high-ways and bridges, thus isolating several towns (Tang et al., 2011). Theevent generated over 500 barrier lakes which threatened people livingdownstream (Fan et al., 2012). Therefore, civil protection actionsaimed atmitigating earthquake damage and at increasing preparednessneed to focus on wide-area evaluations of slope response to strong

audio).

shaking that can be expected under foreseeable seismic hazardscenarios.

The prediction of slope behaviour during future strong earthquakesis made difficult by the complexity of the amplification phenomena re-lated to a combination of topographic and soil/bedrock stratigraphic ef-fects. Topographic effects were invoked by some authors to explainanomalous concentration of landslides triggered by earthquakes nearridge crests (e.g. Harp et al., 1981; Harp and Jibson, 2002; Sepúlvedaet al., 2005). Furthermore, numerical modelling indicated the potentialdestabilising role of topographic amplification (Meunier et al., 2008;Lenti and Martino, 2012). Numerical simulations also provided indica-tions that impedance contrast between surface material and morerigid substratum may cause seismic amplification effects and favourslope failures or landslide reactivation (e.g. Bourdeau and Havenith,2008; Bozzano et al., 2008).

Instrumental evidence of amplification affecting landslide proneslopes have recently been reported in several studies (Del Gaudio andWasowski, 2007, 2011; Gallipoli and Mucciarelli, 2007; Garamboiset al., 2010; Moore et al., 2011), which showed that ground motion

183V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

amplification at site-specific frequencies can present directional maxi-ma close to potential sliding directions (e.g. maximum slope). Thisimplies greater slope susceptibility to seismic failure with respect tothat estimated by standard slope stability analysis.

An example of ground motion amplification is illustrated in Fig. 1,which shows the comparison of horizontal accelerations recorded dur-ing the 6 April 2009 L'Aquila earthquake mainshock (Mw 6.3) in thearea of Caramanico Terme (Abruzzo, central Italy), located about60 km SE of the epicentre. The recordings were acquired at three sites,one located on the head of a pre-existing landslide in colluvial materials(CAR2), the other on themudstone substratum outcrop (CAR1), distantsome 600 m from CAR2, and the third at a reference station on thenearly flat limestone outcrop (CAR4). The analysis showed that, whileon the mudstone slope acceleration was amplified by a factor of twoin comparison to the reference site, a significantly larger amplificationaffected the landslide slope. Furthermore, the landslide site wascharacterised by a pronounced directional peak along a direction closeto that of maximum slope (ENE–WSW). A similar observation was re-ported by Burjánek et al. (2010) who analysed recordings of smallearthquakes at the Randa rock slope (Switzerland). They found a maxi-mum amplification of ground motion on an instable part of the slope,oriented approximately along the steepest slope (Figure 2).

A comprehensive investigation of seismic landslide hazardwould re-quire a long term and diffuse accelerometer monitoring of earthquake-prone regions. However, such widespread monitoring of slopes cannotbe afforded. Although much desirable, the recordings of actual strongmotions affecting slopes are rather few and generally limited to the after-shock phases (Wasowski et al., 2011). The development and applicationof quick and cost-effective reconnaissance techniques based on the anal-ysis of ambient noise can represent a possible solution. One useful recon-naissance technique is the HVNR (horizontal-to-vertical noise spectralratio) or Nakamura method (Nogoshi and Igarashi, 1971; Nakamura,1989). It consists of analysing H/V spectral ratios between horizontaland vertical components of noise recording acquired for a few tens ofminutes. For each component, Fourier spectra are calculated on severaltime windows of few tens of seconds extracted from the recording.Then, after a smoothing, an average of spectral ratios between horizontaland vertical components is derived for all the time windows (for moredetails see the guidelines reported by Bard, 2004).

The HVNR technique is based on the assumption that a strong im-pedance contrast between a surface soft layer and a more rigid substra-tum causes an amplification of the horizontal components of noise

Fig. 1.Comparison of horizontal accelerations recorded at three accelerometer stations in Caraming their location and local geology (right). Explanation: Lm—Miocene and older age stratifiedoritic limestonebreccias and gypsumdeposits (Messinian);Mp—Pliocene agemudstone; Bq—cenumbers from1 to 5 indicate locations of accelerometer stations CAR1, CAR2, etc.; dashed lines sby letters a and b, respectively. CAR2 is sited on the head of the 1989 landslide that mobilisedCAR4 is a reference station located on Miocene limestone bedrock.

groundmotion at the same frequencies at which the shear wave ampli-fication reaches the maximum. Thus, site resonance conditions can berevealed byfinding a pronounced peak in theH/V spectral ratios arounda site specific frequency.

Although the method was originally devised to investigate flat andhorizontally layered sites, it proved capable to reveal resonance proper-ties also in the more complex settings typical of unstable or marginallystable slopes (e.g. Gallipoli et al., 2000; Havenith et al., 2002;Méric et al.,2007; Danneels et al., 2008; Jongmans et al., 2009). In particular, it wasfound that an analysis of azimuthal variation of H/V ratios can reveal thepresence of directional resonance phenomena (Del Gaudio et al., 2008;Burjánek et al., 2010).

Moreover, ambient noise data offer also the possibility to obtain rel-evant information on surface material properties to support evaluationof slope behaviour under seismic shaking. In particular, S-wave veloci-ties can be obtained by deriving surface wave velocity dispersion curvesfrom the processing ofmultiple simultaneous noise recordings acquiredby a geophone array (e.g. Louie, 2001; Ohori et al., 2002) or by a fewportable seismometers; in the latter case one can follow a local scale ap-plication of a correlation analysis technique developed for a broad rangeof distances (cf. Nunziata et al., 2009).

In this paper we provide an overview focused on the informationthat can be obtained from ambient noise analysis for the characterisa-tion of slope stability and slope dynamic response during earthquakes.This is done by considering ourmost recent experiences, aswell as stud-ies published by other workers. We first examine the properties of seis-mic noise to establishwhat part of noise signal can be usefully exploitedwhen investigating landslide prone slopes, and also provide an over-view of the instruments that can be used in field measurements. Thenwe report some recent results obtained from the application of differenttechniques of noise analysis, discuss their potential and limits, and offersome practical implementation guidelines.

2. Ambient noise properties and implications for studying landslideprone slopes

2.1. Frequency range

Ambient noise consists of ground vibrations observed in a wide fre-quency range, non induced by seismic events. Signals at frequenciesabove 1 Hz are commonly indicated as “microtremors” andmainly con-sist of a “cultural noise” generated by human activities (e.g. car traffic,

anico Terme during the 2009 L'AquilaMw 6.3 earthquakemainshock (left) andmap show-limestone; Me—evaporitic succession including clayey–silty–sandy sediments with evap-mented carbonatemegabreccia (LateVillafranchian); Sqh—colluvial deposit (Quaternary);how limits ofmajor landslides, including slope failures occurred in 1989 and 1627markedthick Quaternary colluvium overlying Pliocene mudstone (Mp) on which CAR1 is located;

Fig. 2.Map of spectral amplification (estimated as spectral ratio in comparison to a reference site) at the Randa rock slide slope at three frequencies for ground motion components ori-ented according to the black arrows. Black dots represent the location of seismic sensors and black and yellow lines indicate, respectively, the instability boundary andmain discontinuities(modified from Burjánek et al., 2010).

184 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

trains, machinery at work, industrial plants). Lower frequency compo-nents are produced by natural sources, with oceanic waves and large-scale meteorological conditions acting at frequencies below 0.5 Hz andwinds and local meteorological conditions being responsible for signalsfrom 1 to few Hz (Bonnefoy-Claudet et al., 2006).

The first question to face when planning ambient noise analysis isthe frequency range that can provide information useful for investigat-ing landslide prone slopes. Special attention should be paid to the lowerlimit of the frequency band, as this can guide the choice of the recordinginstruments. Noise recordings should enable extraction of informationabout site resonance frequencies and velocity dispersion curves downto frequencies reflecting S-wave velocities at the maximum depth ofinterest. Thus, the frequency to be analysed depends mainly on theratio between the S-wave velocity Vs of the surface soft layer potentiallysusceptible to mobilisation and its thickness H. If the layer's lateral ex-tension is of the order of ten times greater or more than its thickness,the resonance frequencies fn can be approximated to that of a laterallyinfinite layer, i.e.

f n ¼ 2nþ 1ð Þ � VS

4H½1�

where n is an integer representing the vibration mode. For n = 0, oneobtains the main resonance frequency corresponding to the fundamen-tal mode

f o ¼VS

4H½2�

According to the guidelines provided for the HVNR method by theSESAME project (Bard, 2004), a good definition of H/V ratio peak re-quires an inspection of H/V curve down to a frequency equal to 1/4 offo; thus it is desirable to analyse frequencies down to 1/16 of the Vs/Hratio.

With regard to the survey methods based on the analysis of the dis-persion curve of surface wave velocities, the investigation depth can be

indicated from the 1/2 value of the longest wavelength analysed. There-fore, to obtain information on Vs values down to the base of the surfacesoft layer, one should analyse frequencies at least down to Vs/2H,although an extension of measurements down to 1/2 of such frequencywould be desirable to better constrain the substratum velocity. The iden-tification of the site resonance frequency requires the analysis down tofrequencies around 1/16 of Vs/H, and this frequency requirements ofthe HVNR measurements represents the main constraint on the lowerlimit of the frequency range that can be investigated in ambient noiseanalysis.

Considering the likely values of the Vs/H ratio in landslide proneslopes, inmost cases the analysis ofmicrotremor frequencies can be suf-ficient. However there could be the need to extend observations below1 Hz, especially in the case of very large slope failures, e.g. mega-landslides like the Tsaoling landslide mobilised by the Mw = 7.6 Chi-Chi earthquake of 20 September 1999, characterised by a thickness upto 180 m (Chigira et al., 2003).

Ambient noise frequencies below 1 Hz are dominated by a strongsignal, named “microseismic”, with a major peak around 0.2 Hz(Peterson, 1993). This is commonly defined “double frequency” (DF)peak, in that its frequency is twice that prevailing in ocean waves andconsist of Rayleigh waves excited by the perturbations of sea waterpressure on ocean bottom; this represents an effect of the collision be-tween oppositely propagating waves directed to and reflected by conti-nental coasts (Longuet-Higgins, 1950).

2.2. Polarisation

Polarisation of microseismic signals needs to be considered in ambi-ent noise analysis for its possible influence on the determination of di-rectional properties of site resonance. Analysing seven years ofrecordings acquired all over the world, Schimmel et al. (2011) foundthat the polarisation of microseisms is consistent with a location oftheir sources in ocean areas. They also observed seasonal variations inthe sources positions related to the location of major storms and to

185V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

the consequent changes in ocean swell spatial distribution. This kind ofsignals can propagate over thousands of kilometres. Therefore, an anal-ysis of directional variation of ambient noise H/V ratios at microseismicfrequencies can be biased by the source-controlled Rayleigh-typepolarisation of microseisms, which can show a persistent orientationfor some time and changes from one season to the other.

The above observations indicate a limitation of an analysis of direc-tional resonances at microseismic frequencies. However, according toBromirski et al. (2005), at frequency larger than 0.3 Hz microseismicenergy does not seem to propagate through ocean floor beyond a fewhundreds of km and signals observed at these frequencies are generallyexcited by sea waves generated by winds in the nearest coastal areas.Thus, when analysing site response directivity, frequencies down to0.3 Hz can be taken into consideration, butwith some caution especiallywhere sea coast is close to the study site. In particular, we recommendi) a simultaneous acquisition of noise at different sites in the samestudy area, in order to distinguish whether observed polarisation ispeculiar of certain sites or reflects signal's “regional” properties attri-butable to the characteristics of a distant noise source; and ii) the repe-tition of measurements in different seasons, to reveal whether thepolarisation shows constant properties due to site response and not sea-sonal variations reflecting noise source properties.

2.3. Noise composition

The nature of the wavefields present in ambient noise representsanother aspect that deserves to be thoroughly analysed. The composi-tion of the noise signal is still a matter of a controversy about the actualproportion of different kinds of body and surface waves (cf. Bonnefoy-Claudet et al., 2006; Albarello and Lunedei, 2009). In common practiceof ambient noise analysis it is often assumed that recordings mainlyconsist of fundamental mode Rayleigh waves. However, Bonnefoy-Claudet et al. (2006) showed that, at frequencies N1 Hz, noise verticalcomponents generally include a mix of P waves and Rayleigh waves ofdifferent modes, while horizontal components include a mix of Loveand Rayleigh waves. The proportion between the different types ofwaves varies with frequency and depends on site properties, sourcecharacteristics and distance.

Nevertheless, the uncertainty regarding noise wavefield nature doesnot compromise the detection of the resonance frequency. The detec-tion is feasible at least in case of resonance caused by impedance con-trast in a 1D layering, because, at a similar frequency, a maximum ofH/V ratio is observed both in bodywaves, as effect of S-wave amplifica-tion, and in surface waves, as effect of the vanishing of Rayleigh wavevertical component (Bard, 1999). This is why consistent results areobtained in the resonance frequency identification.

However, the recognition of the noise signal nature is essential toextract additional information for the determination of amplificationfactors, considering the influence of different wavefields on the ampli-tude of the H/V ratio values (see Albarello and Lunedei, 2009). Thus,with regard to the interpretation of H/V curves, it is desirable to developa reliable technique to extract, within the noise recording, the wave-trains of different types, identifying, in particular, the Rayleigh waveswhose H/V curves can be conveniently modelled in terms of ellipticityof the Rayleigh wave particle motion.

With regard to the interpretation of dispersion curves derived fromnoise data, the use of vertical component recordings should lead to reli-able identification of Rayleigh waves, because in this case Love wavesare excluded and body waves have different velocities. However, someuncertainties can remain about the vibration mode of the recordedRayleigh waves and, in general, the inversion procedure requires con-siderable caution. For instance in some applications it may be necessaryto test different hypotheses and conduct a multimodal inversion inwhich different parts of the dispersion curve corresponding to anoma-lous velocity changes are tentatively attributed to different modes(e.g. Coccia et al., 2010).

3. Instruments for noise measurements

Different instruments are suitable for ambient noise measurements,depending on the frequency band of interest and the data processingprocedure. For HVNR measurements portable velocimeters witheigenfrequency of 1 to a few Hz can be used for microtremor analysis,whereas accelerometers should be excluded because they are not sensi-tive enough to record small amplitude ground vibration. The marketnow offers specifically devised compact meters of noise, named“tromographs”, e.g. Tromino (see www.tromino.eu for details), whichcan record signals down to frequencies of a few tens of Hz.

If the focus is on frequencies below 1 Hz, a better option could be touse a portable broad-band sensor like Trillium Compact (produced byNanometrics) combined with a portable acquisition system: despiteits small size (of the order of 10 cm), inner electronic feedback circuitsallow extending sensor response to very low frequencies, keeping thenominal amplitude response practically homogeneous in a widefrequency range (differences not larger than 1% in the interval 0.02–50 Hz). The electronic feedback makes this instrument very sensitiveto environmental conditions (temperature, air pressure, supportingsurface deformations), whose variation during a measurement canintroduce additional noise at low frequencies. Such effects can bemitigated by insulating the sensor under a foam-lined cover duringmeasurements.

The time needed to stabilise broad band sensors response from hys-teretic drift induced by shocks during instrument transfer betweenmeasurement sites can represent a potential practical limitation. How-ever, we carried out several tests comparing two identical Trillium in-struments placed side by side, one kept fixed at a permanent seismicstation and the other moved around before starting data acquisition.The test results demonstrated that after 10–15 min the “mobile” Trilli-um response stabilises, becoming equal to that of the fixed instrument(Figure 3); furthermore, applying a linear detrend to each of the timewindows for which spectral ratios are calculated, the drift effects canbe removed from the data acquired within a few minutes after instru-ment installation.

Noise measurements aimed at deriving Rayleigh wave velocity dis-persion curves require simultaneous recordings by multiple properlysynchronised sensors (Shapiro and Campillo, 2004). For investigationsinvolving relatively long distances (in the order of several hundreds ofmetres), this can be obtained by deploying two or more compact seis-mometers or tromographs, whose recordings are synchronised byGPS.When dealingwith shorter distances, one can use geophone arrays,selecting vertical sensors with eigen-frequencies as low as possible(e.g. 4.5 Hz), according to the recommendations provided by Louie(2001).

4. Determination of site resonance properties

The HVNR technique is commonly used to characterise site reso-nance properties in simple geological conditions that can be assimilatedto 1D layering. Although unstable slopes frequently show lateral varia-tions in material properties, often the depth of a surface layer suscepti-ble to sliding is small in comparison to its lateral extension. In thesecases the analysis of H/V spectral ratios should reveal resonance fre-quencies related to the combination of surface layer thickness andmean Vs velocity (see Eq. (2)). Literature on landslides provides severalexamples of the HVNR technique applications, in which the Eq. (2) andthe observed variations of resonance frequencies were used to estimatelateral variations of landslide thickness. For instance, Danneels et al.(2008), studying a thin, about 1 km long loess flow in Kyrgyzstan,exploited changes in H/V peak frequencies to reveal that the thicknessof the mobilised layer varied from 3 to 12 m. Other examples of similarapplicationswere reported by Gallipoli et al. (2000), Méric et al. (2007),Jongmans et al. (2009) and Torgoev et al. (2013).

0

5

10

15

Fre

quen

cy (

HZ

)

0 10 20 30 40 50

time (minutes)

a) REFERENCE

0

5

10

15

Fre

quen

cy (

HZ

)

0 10 20 30 40 50

time (minutes)

b) ROVER

0 2 4 6 8 10

Ratio

Fig. 3. Comparison of spectrograms of H/V ratios obtained for the North component of groundmotion from the recordings of two Trillium instruments placed side by side. Spectral ratiosas function of frequency were calculated on successive 30 s windows and are represented as vertical bars through a colour scale as function of time, starting from the initial recording:a) spectrogram of the permanent station; b) spectrogram of the “mobile” station.

186 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

However, the resonance properties of slopes affected by or suscepti-ble to landsliding often appear more complex than those observed insites with simple horizontal layering. This is not surprising consideringthat resonance phenomena are basically caused by constructive inter-ference of waves reflected–refracted–diffracted at the free surface andat interfaces separating materials characterised by strong impedancecontrast. The geometry of these surfaces can determine amplificationat multiple frequencies controlled by the topography and by the lateralextent (in addition to thickness) of near-surface geological bodieswhich trap seismic waves. Indeed 2D or 3D effects were found to leadto complex pattern of resonance peaks in case of larger scale geologicalfeatures like sedimentary basin (cf. Haghshenas et al., 2008) and onecan expect that similar situation can also occur, at higher frequencies,on slopes affected by landslides.

4.1. Identification of site response directivity

One consequence of the complex site response in landslide-proneareas is the occurrence of resonance anisotropy reflected by azimuthalvariation of the H/V spectral ratios. The occurrence of directional vari-ability of site response has been reported in literature (e.g. Bonamassaand Vidale, 1991; Spudich et al., 1996) in different geological and topo-graphic conditions and, interestingly, in some cases the relation to thepresence of pre-existing landslides was postulated (cf. Rial, 1996; Xuet al., 1996). Phenomena of resonance directional variability havebeen taken into consideration by some workers who applied theHVNR techniques to study landslide areas (e.g. Havenith et al., 2002;Méric et al., 2007). In particular, the differences in H/V ratios calculatedalong orthogonal directions have been interpreted as possibly relatedto landslide or slope characteristics (e.g. slope direction, maximum/minimum thickness variability).

The results of a first comprehensive directional analysis of ambientnoise recordings to detect H/V anisotropies in landslide areas were re-ported by Del Gaudio et al. (2008). Their study focused on the testarea of Caramanico Terme (central Italy), where an ongoing long termaccelerometer monitoring of landslide prone slopes offered the oppor-tunity of comparing the results of noise analysis with site amplificationscharacteristics inferred from seismic event recordings. In particular,following the methodology proposed by Borcherdt (1970), the analysisof the Standard Spectral Ratios (SSR), i.e. the average spectral ratiosbetween homologous component recordings of the same events on

different slopes and at a reference site on rock, revealed that somesites were constantly characterised by pronounced directional maximaof amplifications along a site specific preferential direction, regardlessof the event source location and mechanism (Del Gaudio andWasowski, 2007, 2011). The HVNR analysis at the same sites showeda good correlation with systematic directional maxima of H/V spectralratio approximately oriented as the site response directivity revealedby SSR analysis (Del Gaudio et al., 2008, 2013). A similar consistencyof the results was reported in the case of the Randa rock slide(Switzerland), where, in the unstable part of the slope, an analysisaimed at identifying strike and ellipticity of noise polarisation, revealeda persistent maximum of polarisation oriented as the SSR maxima(Burjánek et al., 2010; Moore et al., 2011).

On the basis of our experiences, we propose the following diagnosticcriteria for the reliable recognition of the occurrence and the orientationof site response directivity fromHVNRmeasurements (Del Gaudio et al.,2008, 2013):

1) Presence, in the average H/V spectral ratios, of relative maximawithamplitude larger than 2;

2) Azimuthal variation of H/V ratios of such maxima, with their ampli-tude at the peak frequency showing a decrease down to a directionalminimum (typically orthogonal to maximum), which should not belarger than 2/3 of the maximum;

3) Consistent orientation (within 20°–30°) of major directional peaksin the average H/V ratios;

4) Dominant presence (in quantitative terms), in the recording session,of time windows showing directional peaks satisfying the criteria1) and 2) along a common orientation.

The criterion 1) is diagnostic with regard to the presence of amplifi-cation conditions: the H/V ratio at bedrock is expected to be equal to 1within an approximation factor of 2, thus H/V N 2 values are requestedto evidence that surface lithology and/or morphology can cause groundmotion amplification. The criterion 2) is requested as evidence of asignificant anisotropy of site response and the criterion 3) allowsrecognising the presence of a direction along which maximum groundmotion tend to be concentrated.

Finally, the criterion 4), introduced under the acronym DHVPOR(Directional H/V Peak Occurrence Rate: see Del Gaudio et al., 2013), as-sures that H/V directional maxima reflect the persistence of a coherentpolarisation during the recording session, excluding any bias by the

187V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

temporary recording of a strongly polarised wave train. In standardHVNR method applications this bias is corrected by eliminating fromthe calculations of average H/V ratios those time windows that can beresponsible for a strong increase of standard deviation of spectral ratios(cf. Bard, 2004; Castellaro and Mulargia, 2009). However such a proce-dure could be too restrictive in the case of HVNR directional analysis

020 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

a) Jul 2007

020 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

c) May 2011

0 2 4

Fig. 4. Histograms of the DHVPOR values calculated from noise recordings carried out at site CAshowdirectional peaks satisfying significance criteria (see text), for different combinations of azthe average of the amplitudes of the H/V peaks belonging to each azimuth-frequency bin; noteceed the colour scale maximum.

because the variability of directional H/V ratios is larger than that oftheir average between horizontal components; this greater variabilityis due to noise polarisation controlled by properties of sources sparselydistributed around the measurement site. Thus, the criterion 4) is a“softer”wayof correcting the bias that can result from isolated polarisedwave trains.

020 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

20

b) Jun 2010

020 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

d) Jun 2012

6 8 10

mean H/V

R2 at different times. Bar heights are proportional to the percentage of time windows thatimuths (spaced by 10°) and frequency (binned by 0.5 Hz intervals). Colour scale representsthat dark bars represent azimuth-frequency bins for which the average H/V peak ratio ex-

188 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

Finding all the 4mentioned criteria satisfied in a single noise record-ing, however, does not assure that the studied site is characterised bysite directional resonance. Indeed it is possible that environmental con-ditions determine the presence of a polarised noise spanning throughseveral frequencies: for instance, thepresence of a continuous dominantwind can induce vibration of trees, poles, buildings of different sizes,causing noise with coherent polarisation at different frequencies. Thusnoise recordings should be repeated at different times and under differ-ent environmental conditions to distinguish a constant site specific

020 40 60 80 100120140160180

Azimut

02

46

810

1214

16Frequency (Hz)

1020

3040

5060

7080

9010

0

Perce

ntage

a) Jul 2007

020 40 60 80 100120140160180

Azimut

02

46

810

1214

16Frequency (Hz)

1020

3040

5060

7080

9010

0

Perce

ntage

c) May 2011

0 2 4

Fig. 5. Histograms of the DHVPOR values represented as in Fig. 3, derive

pattern of H/V peak polarisation from occasional conditions of peakiso-orientation controlled by the noise sources.

Figs. 4 and 5 show an example of this approach through 3D histo-grams representing the distribution of DHVPOR values as function ofazimuth and frequency: in these diagrams bar height is proportionalto the percentage of time windows including significant directionalpeaks (in the sense of criteria 1 and 2) and colours represent the av-erages of H/V peak values falling within each azimuth-frequencybin.

020 40 60 80 100120140160180

Azimut

02

46

810

1214

16Frequency (Hz)

1020

3040

5060

7080

9010

0

Perce

ntage

b) Jun 2010

020 40 60 80 100120140160180

Azimut

02

46

810

1214

16Frequency (Hz)

1020

3040

5060

7080

9010

0

Perce

ntage

d) Jun 2012

6 8 10

mean H/V

d from noise recordings carried out at site CAR5 at different times.

189V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

DHVPOR values shown in Figs. 4 and 5 were obtained from differentmeasurement campaigns carried out at two nearby sites of theCaramanico test area, i.e. the already cited accelerometer station CAR2,located on a landslide, and another accelerometer station, CAR5, placedon the same material of the landslide but about 150 m upslope of thelandslide crown (see Figure 1 for location).

Accelerometer data revealed no systematic preferential orientationof maximum acceleration is present at CAR5, which is contrary towhat was observed at CAR2. The results of noise analysis also showeda preferential orientation of H/V peaks at CAR2 in all the noisemeasure-ment campaigns, consistent with that of seismic ground motion ampli-fication (Figure 4), and amore disperse and time varying distribution ofH/V peaks at CAR5 (Figure 5). However the results of a campaign in2012 indicated a preferential orientation of peaks at CAR5 as well, sothat on the basis of this single measurement one could erroneouslyinfer the presence of a site response directivity. This example underlinesthe importance of measurement repetitions.

4.2. Identification of directional resonance frequency

Although the presence and orientation of a site response directivitycan bequite easily recognisedwith a few repetitions of noise recordings,the identification of main directional resonance frequencies is notalways straightforward. We can expect difficulties in at least four situa-tions: a) the presence of 2D or 3D effects; b) the occurrence of amplifi-cation of the vertical component of groundmotion; c) the presence of aweak excitation of Rayleigh waves by the ambient noise source; d) thepresence of a strong signal polarisation controlled by noise sourceproperties.

Site resonance properties can be complex in the presence of near-surface geological bodies whose geometry significantly deviates from1D layering. With reference to larger scale structures like sedimentarybasin, Haghshenas et al. (2008) showed that the spectral amplificationof seismic waves tends to be complicated by the superimposition of in-terference between waves travelling not only vertically between freesurface and substratum top surface, but also horizontally between thelateral boundaries of the basin. On the other hand, ambient noise H/Vspectral ratios, when measured near the edge of 2D or 3D geologicalbodies, show lower and broader maxima in comparison to those ob-served on a 1D layering of the same thickness. These phenomena cancause considerable dissimilarities between the curves of H/V ratios

a) HVNR

0

5

10

15

Fre

quen

cy (

HZ

)

0 30 60 90 120 150 180Azimuth

Fre

quen

cy (

HZ

)

0 5

Fig. 6. Comparison between azimuthal variations of HVNR (left) and SSR (right) values at CAR2averaging spectral ratios between recordings obtained at CAR2 and at the reference site CAR4values relative to the vertical component.

and those of spectral amplification, and this makes the identificationof the main frequencies affected by amplification more uncertain.

One can expect that similar situations can occur also at the scale ofsmaller geological features like landslides. For example, on the basis ofan extensive microseismic survey of the Kalai Nav landslide in TienShan, Torgoev et al. (2013) indicated a considerable reduction of H/Vspectral ratio amplitude in the head and crown zone in comparison toits central part. Also our observations of H/V curves obtained from theCaramanico accelerometer site CAR2 at the landslide head confirmedboth the presence of i) a more complex SSR pattern (in comparison towhat could be expected on the basis of landslide thickness and imped-ance contrast only), and ii) discrepancies between amplitude of H/Vand SSR peaks, the former being much lower than the latter (seeFigure 6).

The recognition ofmain resonance frequencies fromH/V spectral ra-tios of noise recordings is further complicated by the possible occur-rence of vertical component amplification, which can reduce the H/Vratios. Such amplificationwas observed at CAR2 site just at the frequen-cy of about 2.5 Hz characterised by the highest level of directional am-plification of the horizontal components (see Figure 6b and DelGaudio et al., 2013). Furthermore, measurement repetitions at differenttimes revealed that theH/V peak at 2.5 Hz, evident in the June–July data,was hardly distinguishable in other periods; this in turn implies a possi-ble influence of seasonal variations (Figure 7).

This phenomenon could perhaps be linked to the variation in watercontent of colluvial deposits constituting the landslide body at CAR2.The increase in water content following rainfall and snow melting inwinter–springmonths could be responsible of the rise of P-wave veloc-ity: this, in turn, could result in an increase of H/V ratio, both for the re-duction of the amplification of body wave vertical component and forthe flattening of the ellipticity of Rayleigh wave ground motionresulting from the Poisson ratio increase (cf. Tuan et al., 2011).

Other difficulties in recognition of directivity can arise from theweak excitation of Rayleigh waves by the ambient noise sources. Ray-leigh waves appear the most effective source of information on site di-rectional resonance, but the results of DHVPOR analysis have providedevidence that a large portion of a noise recording can contain limitedamount of well polarised wave-trains of Rayleigh type (Del Gaudioet al., 2013). This means that the average of H/V spectral ratios is less-ened by the contribution of non-polarised signals, which do not reflectsite directional resonance properties. The implication is that a carefulselection of Rayleigh wave trains in the noise recording should lead to

b) SSR (23 events)

0

5

10

15

0 30 60 90 120 150 180Azimuth

Z

10

Spectral ratio

site. HVNR derived from noise measurements carried out in July 2007; SSR values derivedfor 23 seismic events recorded from 2007 to 2009. Vertical bar to the right shows the SSR

0

5

10

15

Fre

quen

cy (

HZ

)

0 30 60 90 120 150 180Azimuth

Jul 2007

0

5

10

15

Fre

quen

cy (

HZ

)

0 30 60 90 120 150 180Azimuth

Jun 2010

0

5

10

15

Fre

quen

cy (

HZ

)

0 30 60 90 120 150 180Azimuth

May 2011

0

5

10

15

Fr

eque

ncy

(HZ

)

0 30 60 90 120 150 180Azimuth

Dec 2011

0

5

10

15

Fre

quen

cy (

HZ

)

0 30 60 90 120 150 180Azimuth

Jul 2013

0 1 2 3 4 5 6

mean H/V

Fig. 7. Azimuthal distribution of HVNR values derived from noise measurements at site CAR2 repeated in different periods of year.

0

2

4

6

8

10

Rat

io

0 2 4 6 8 10 12 14 16

Frequency (Hz)

SSR 90°

HVNR 2007 Tromino

HVNR 2010 Trillium

HVNR 2011 Tromino

HVNR 2011 Trillium

mean HVNR

Fig. 8.Diagram ofmean values of peak spectral ratios at site CAR2 along a direction (E–W)characterised by the highest DHVPOR values. The red solid line represents the SSR valuesobtained along the same azimuth; the other lines represent the average of the H/V peaksvalues in timewindows of different recordings (specified in the legend), grouped into fre-quencies bins of 0.5 Hz. The thick solid line represents the average of the curves relative todifferent measurements.

190 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

the improvement of analysis and better results. In this context, someworkers proposedmethods based on time-frequency analysis to identi-fy signal portion having a significant energy in the vertical component(Fäh et al., 2001; Poggi et al., 2012), and thereby exclude the presenceof significant contribution from SH-type polarised signals (Love orbody waves) to noise wavefield.

A simplified method to enhance the contribution of Rayleigh wave-trains to the H/V ratio calculation was proposed by Del Gaudio et al.(2013) in the framework of the DHVPOR analysis. According to thismethod, the calculation of the average H/V ratios is restricted to thepeak values (found in each recording session time window) that satisfythe significance criteria 1) and 2) described in Section 4.1. This excludesH/V ratios related to non-polarised signals. Applying this method to thenoise data from CAR2 site, the presence of a significant resonance fre-quency at 2.5 Hz can be highlighted despite the considerable fluctua-tions of H/V ratio amplitude related to seasonal variation of siteconditions (Figure 8).

Uncertainty in directional resonance frequency analysis can derivealso from the possible presence of a source of strongly polarised noiseacting during data acquisition. This will be especially relevant at rela-tively low frequencies, which are characterised by a lower attenuationand can be recorded even at large distance from the noise source. Thiscan be the case of dominant winds blowing in an almost constant direc-tion or seawaves impact on the nearest coast. A procedure that can helpdistinguish such cases from the occurrence of site directional resonanceconsists in acquiring data simultaneously at more sites.

In particular, with at least two noise meters available, one of themcan be conveniently maintained in continuous recording at a fix siteused as reference, while the other one is employed as a “rover” andmoved to different sites for shorter recordings. A comparison betweensimultaneous recordings at the reference and the rover sites can revealwhether coherently polarised signals reflect site specific resonanceproperties or are simply due to a “regional”wavefield coming from ex-ternal sources and acting throughout the study area.

To illustrate the above case we present an example from our studyconducted in the area of the Terano landslide, Japan. The failure, trig-gered on 23 October 2004 by the mid Niigata prefecture earthquake

(Mw = 6.6), affected a slope with alternating beds of sandstone andsiltstones, and caused the damming of the Imogawa river (Chigira andYagi, 2006; Sassa et al., 2006). To recognise site specific resonance fre-quencies we analysed the DHVPOR values from recordings carried outsimultaneously with two instruments. One of these was kept fixed atsite (TJ0), located at the toe of the landslide and used as reference.Fig. 9 shows an example relative to the comparison with the recordingsacquired at a site (TJ1) on the head of the landslide.

0 20 40 60 80 100 120 140 160 180

Azimut

0 20 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

02

46

810

1214

Frequency (Hz)

1020

3040

5060

7080

9010

0

Per

cent

age

a) TJ0

16

1020

3040

5060

7080

9010

0

Per

cent

age

b) TJ1

0 2 4 6 8 10

mean H/V

c) DHVPOR TJ0 - TJ1

0

10

20

30

40

50

60

70

80

90

100

DH

VP

OR

0 2 4 6 8 10 12 14 16

Frequency (Hz)

0

2

4

6

8

10

H/V

Rat

io

TJ0

TJ1

Fig. 9. Histograms of DHVPOR values obtained for noise measurements carried out in 2012 on the slope affected by the Terano landslide (Japan). Part a) and b) represent the results ob-tained from simultaneous noise recordings carried out, respectively, at the sites TJ0, on the landslide toe, and TJ1, on the landslide head (see Figure 10 for location). For the directioncharacterised by the highest DHVPOR values, part c) shows a comparison of data of two stations distinguished by colours (red for TJ0, blue for TJ1), i.e.: the DHVPOR values of TJO andTJ1 indicated as bars put side by side within each frequency bin of 0.5 Hz (scale to the right); the average (solid lines) and the average ±one standard deviation (dashed lines) of H/Vpeak values calculated as in Fig. 8 (scale to the left).

191V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

Following the above described procedure, curves of the average H/Vpeak values for frequency bins of 0.5 Hz were examined along an ap-proximately E–Wdirection which is characterised by a preferential ori-entation of H/V directional peaks. This was done assuming as significantthose H/V peaks corresponding to high values of DHVPOR (to excludepeaks derived from measurements including a small number of timewindows). Apart from a strong polarised signal at frequencies below 1Hz, which appears ubiquitous at all the measurement sites and is likelyrelated to a source-controlled polarisation, the results showed two

distinct significant peaks (with H/V ratio ≈ 5) at 2.5–3 Hz and 3.5–4 Hz, respectively for TJ0 and TJ1. Other peaks at TJ1 at frequencies of12 and 13 Hz are not considered significant, because the correspondinghigh H/V ratios (with amplitude of about 4) are derived from only about5% of the recording data (Figure 9). Such low percentage suggests thatthese peaks reflect the strong polarisation of a short duration wave-train rather than a site effect.

Fig. 10 synthesises the results of the ambient noise measurementcampaign in terms of orientation and frequencies of significant H/V

Fig. 10.Map of the Terano landslide and results of ambient noise analysis in terms of direction (indicated by arrows) and frequencies (indicated by colours) of main H/V directional peaksderived from theDHVPORanalysis. Black arrows indicate that H/V peakswere foundonly at frequencies less than1Hz. Solid anddashed lines outline the boundary of 2004 landslide andofa previous slope failure.

192 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

directional peaks. Although local site-specific variation of peak frequen-cies is apparent throughout the slope affected by the landslide, all peaksshare a common preferential E–W orientation. However, to verifywhether they reflect sites' directional resonance frequency, a repetitionof measurements under different environmental conditions is neededto exclude the influence of localised source of polarised noise (e.g. dif-ferent height trees shaken by constantly directed wind).

5. Determination of S-wave velocity models

Although in some cases the amplitude of H/V spectral ratio wassimilar to SSR values (cf. Lermo and Chávez-García, 1994), the formercannot be confidently used as a reliable estimate of site amplificationfactor at the resonance frequency. Typically a correlation is found be-tween H/V ratio amplitude and spectral amplification, considering thatboth increase with the impedance contrast between surface layer andsubstratum (Fäh et al., 2001). However, in general, the values of theH/V spectral ratios can considerably differ from the site amplificationfactor, being influenced by the ellipticity of the Rayleigh waves and bythe amount of SH-type wave (Love and body waves) contributing tothe ambient noise. The relative weights of different body and surfacewaves composing the noise wavefield were found to depend on certaincharacteristics of themeasurement sites (e.g. presence of a more or lesslarge impedance contrast, interface geometry deviation from 1Dlayering) and of the noise sources (e.g. distance from the measurementsite) (Haghshenas et al., 2008).

Nevertheless, amplification factor can be estimated through a nu-merical modelling of slope behaviour under seismic shaking, whichcan be incorporated in dynamic slope stability assessment, e.g. throughpermanent displacement analysis techniques that represent a goodcompromise between prediction reliability and computational simplic-ity (Jibson, 2011). To include the effect of slope dynamic response, suchmethods use “decoupled” and “coupled” approaches, according towhether site amplification is assessed before or during permanent dis-placement computation. In both cases the modelling of slope dynamicresponse depends primarily on elastic characteristics of slope materialsthat can be derived from or represented by shear wave velocities of lith-ological layers present above and below the slip surface (Jibson, 2011).

Several computer programmes are available to calculate seismicground motion amplification. For example, one can use 1D modelling(e.g. STRATA by Kottke and Rathje, 2008), which is adequate when am-plification is due only to impedance contrast between surface layer and

bedrock, or 2D finite element analysis (e.g. QUAD4M, Hudson et al.,2003), which in addition can account for topographic amplificationsand boundary effects. The main required input is shear modulus orS-wave velocity of the geological bodies beingmodelled as constitutingthe site subsoil.

S-wave velocities are commonly obtained using several methods ofactive seismic survey, however, passive methods based on ambientnoise processing can also be used. The latter offer some practical advan-tages, because they do not require an artificial source of seismic waves,which can present logistic and safety problems in the context of unsta-ble slopes and rough topography typical of landslide areas.

Ambient noise processing aimed at Vs determination can be con-ducted in different ways using different instruments and acquisitionprocedures. In particular, one can derive velocity model i) by inter-preting the same H/V curve as function of frequency, ii) from analysisof correlation between simultaneous recordings carried out accordingto different configurations of deployed sensors, and iii) from thespace-time sampling of the noise wavefield along a geophone arrayaimed at pointing out signal corresponding to passing through Rayleighwave trains.

5.1. H/V curve modelling

The inversion of H/V ratio spectrum in terms of S-wave velocity canbe carried out searching velocity models capable to reproduce the ob-served H/V curve in numerical simulations (e.g. Arai and Tokimatsu,2004). In the “trial and error” procedure the search of solutions compat-ible with the observation data (within the measurement uncertainties)relies on assumptions regarding the presence and proportion of differ-ent wave types (S-waves, different modes of Rayleigh and Lovewaves) in the ambient noise recording. Furthermore, some independentconstraints are desirable (e.g. the depth of a major vertical discontinu-ity) to limit the number of possible solutions (Castellaro and Mulargia,2009).

One advantage of the above method is that it is less influenced bylateral variation of soil properties with respect tomeasurements carriedout with a sensor array, because H/V values are based onmeasurementscarried out at a single point. However, one should take into account that,as previously noted, H/V curve can be altered by 2D/3D effects in case ofmeasurement sites located close to lateral boundaries of geologicalbodies.

193V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

A major drawback of the method is the dependence of H/V curveinterpretation on a considerable number of variables (including theP-wave velocities), which multiplies the number of possible modelscompatible with the experimental data. Thus, the method should notbe used without the support of other data and/or techniques, but canbe best exploited to provide additional constraints needed in othergeophysical investigations.

5.2. Correlation methods

A secondmethod to determine Vs values from noise data processingconsists in calculating the cross-correlation between simultaneousnoise recordings at couple of sensors deployed in the study area. Thecalculation is carried out by averaging the cross-correlation obtainedfor a large number of time windows extracted from a long recording:these timewindows need to be several times longer than themaximumperiod to be analysed. The cross-correlation obtained for different timeshifts between the recordings points out coherent wave-trains travel-ling from one sensor to the other and this allows calculating the velocityof such wave-trains. By analysing the velocity for different frequencies,it is possible to recognise dispersive surface waves and determine theirvelocity variations with frequency, which mainly depend on verticalchanges in Vs.

To avoid ambiguous identification of surface waves, vertical compo-nent sensors should preferably be used, thus excluding Lovewaves fromdata acquisition. The obtained velocity dispersion curves can be theninterpreted as pertinent to Rayleigh waves only and inverted to deter-mine the Vs vertical distribution according to a 1D model.

This kind of an approach was first proposed by Aki (1957) and isnow known under the acronym SPAC (Spatial Auto-Correlation). Theapplication requires the deployment of circular arrays of sensors arounda central sensor, which allows obtaining an azimuthal average of thenormalised correlations between the recordings at the central sensorand at each of the other equidistant sensors. Aki (1957) showed thatthe correlation between a couple of recordings filtered around definedfrequencies has the form of a zero order Bessel function of the firstkind, whose argument is the product of the distance between sensorsby the wave number associated to that frequency: thus the wave num-ber for different frequencies can be found and phase velocitydetermined.

One challenging aspect of the application of this method is the needto deploy the arraywith a circular configuration; thismay not be easy inlandslide areas. Furthermore, since the results reflect an azimuthal aver-age, the method cannot reveal anisotropies of mechanical properties inslope materials.

One recent application of this technique to unstable slopes was re-ported by Méric et al. (2007), who investigated two landslides in theFrench Alps. The obtained results were in agreement with those of theactive seismic survey, and the authors also used the outcomes of H/Vmeasurements to provide additional constraints for the dispersioncurve inversion.

The difficulty of deploying the sensors array according to a circularconfiguration can be overcome following a methodological develop-ment known under the acronym ESAC (Extended Spatial Auto-Correlation), which was proposed by Ling and Okada (1993) andpopularised byOhori et al. (2002). Through this technique simultaneousnoise recordings acquired by different sensors are first filtered arounddifferent central frequencies. Then, for each analysed frequency, crosscorrelation is calculated between recordings obtained at different cou-ples of sensors and compared to a Bessel function expressed as functionof sensor distance and wave velocity. Finally, for the latter parameterthe value is searched that provides the best fitting of the Bessel functionwith the cross correlation values obtained for different inter-sensor dis-tances. This approach requires the deployment of an array of sensorsdistributed at different distances from each other in order to have

enough cross-correlation values for a reliable estimate of velocity pro-viding the best fitting.

However, recently Ekström et al. (2009) proposed an alternativeway to obtain Rayleigh wave velocities as function of frequency. Inthis alternative distinct dispersion curves can be calculated for eachcouple of sensors and mean Rayleigh wave velocity along the pathconnecting the two sensors is obtained. Most recently Pilz et al.(2013) used this approach to obtain, from a set of multi-sensor ambientnoise recordings, 3D tomographies of S-wave velocities within a slopeaffected by a landslide in the Fergana valley (Kyrgyzstan).

Another technique exploiting a cross-correlation analysis is based onthe properties of random diffuse wavefields demonstrated by LobkisandWeaver (2001) in acoustics. The technique was first applied in am-bient noise analysis by Shapiro and Campillo (2004), who found thatfrom cross correlation between noise recordings carried out simulta-neously at two stations one can derive a signal proportional to theGreen's function relative to the station pair, i.e. the signal recorded atone of the station as effect of an impulsive instantaneous force appliedat the other station site. The noise signal can be processed with afrequency-time analysis (FTAN technique: Dziewonski et al., 1969;Levshin et al., 1972) to obtain a dispersion curve of surface wavegroup velocities, which, in turn, can be inverted in terms of S-wave ver-tical distribution. This approach has recently received an increased at-tention for its application potential to study different scale geophysicalphenomena using a variable frequency range (from crustal-mantlestructure to local site investigations: see Nunziata et al., 2009). A com-prehensive description of the data processing methodology can befound in Bensen et al. (2007).

In principle, velocity measurements can be obtained from a singlepair of sensors. However, a potential problem derives from the assump-tion of isotropic distribution of noise sources, which is plausible consid-ering that recorded wavefield is scattered by sparsely distributedsubsoil heterogeneities. The isotropy assumption is imposed by themethod theory to obtain from data processing real velocities ratherthan apparent ones biased by the absence of waves propagating parallelto the line joining the sensor pair. Recent experimental tests demon-strated that the isotropy of ambient noise source distribution is hardlysatisfied (Mulargia, 2012). However, with multiple sensors deployedto secure a coverage of different azimuths in noise data sampling, thebias of an anisotropic distribution of noise sources can be containedthrough a proper data processing (Mulargia and Castellaro, 2010).

The cross-correlation analysis of ambient noise can use data ac-quired for H/V spectral ratio calculations provided that simultaneousreference-rover recordings rely on an accurate synchronisation system.Thus the acquired data can be exploited for two different types of anal-ysis. In the context of landslide area investigation, however, one canhave to face the presence of an anisotropy both in noise source azimuth-al distribution and in slopematerial properties, so that it is not simple todistinguish between these two effects.

Fig. 11 shows the first results obtained in the Caramanico study areaby applying the cross-correlation method to noise recordings carriedout simultaneously at three sites: CAR2 on the slide head and at twoother sites located outside the landslide, one (CAR5) located 150 maway upslope of CAR2 and the other (G12) 180 m away along theslope direction. The data were processed using codes included in thesoftware package CPS (Computer Programs in Seismology: Herrmann,2010). Importantly, the application of FTANmethod allowed the recog-nition of Rayleigh waves of two different modes (fundamental and 1sthigher mode), which helped to better constrain Vs velocity inversion.The results of the inversion showed a significant difference in velocity(up to 40% in shallow layers) between the alignments parallel and per-pendicular to the maximum slope direction, the former beingcharacterised by lower velocity. We cannot exclude that this differenceis an artefact due to anisotropy of noise sources, which would tend tocause an overestimate along alignment lacking noise sources. Howeverthe difference in velocity appears consistent with the local structural

Fig. 11. Results of the inversion of the dispersion curves of Rayleighwave group velocities derived from cross-correlation analysis carried out between the recording site pairs CAR2–CAR5and CAR2–G12: a) Vs velocity vertical profile providing dispersion curves best fitting the experimental data for the pair CAR2–CAR5 (blue line) and CAR2–G12 (red line); b) location of thetwo investigated alignments on a DEM of the study area showing lithological boundaries (white lines); c) and d) theoretical curve (continuous line) and experimental values (dots) ofRayleigh wave group velocity for CAR2–CAR5 and CAR2–G12, respectively (note that the two distinct curves in each diagrams are referred, from the right to the left, to the fundamentaland the first higher mode).

194 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

setting characterised by the presence of discontinuities roughly perpen-dicular to the maximum slope direction (Wasowski and Del Gaudio,2000).

An interesting application of correlation-based method of velocitydetermination has recently been reported by Mainsant et al. (2012),who conducted continuous monitoring of ambient noise on two sitesnear the opposite lateral boundaries of the Pont Bourquin landslide inSwitzerland (Figure 12). It is a case of about 10 m thick earthflowmobilised after intense rainfall on August 2010. The continuous acquisi-tion of ambient noise offered the possibility of measuring the time var-iation of Rayleigh waves in slope material on a daily basis. The analysisof velocity variation at frequencies of 10–12 Hz showed a continuousgradual decrease during themonth preceding the failure and an acceler-ated drop in velocity (about 7%) in the last 4 days before the landslideactivation (Figure 12). Numerical modelling showed that the changein Rayleigh wave velocity is attributable to a decrease of Vs in anabout 2 m thick layer at the base of the earthflow; this can be related

to weakening of slope material shear strength. The case study reportedby Mainsant et al. (2012) suggests an interesting potential of ambientnoise measurements for implementation in slope failure warningsystems.

5.3. Geophone array methods

Another approach that can exploit ambient noise to determine slopematerial Vs values consists in the application of a survey method devel-oped by Louie (2001), commonly known under the acronym ReMi(RefractionMicrotremor). This methodmakes use of an array of verticalgeophones of relatively low frequency (typically about 4 Hz), which ac-quire ambient noise for some tens of minutes. Then recordings aresubdivided into time windows of few tens of seconds. Each set of re-cordings at different geophones represents a space-time sampling ofnoise wavefield: data are processed to transform them into a matrix ofmean normalised spectral powers which is named p–f transform. It is

Fig. 12. Location of two stations (S1 and S2) of ambient noise continuousmonitoring at theopposite sides of the Pont Bourquin landslide (top) and diagram showing Rayleigh wavevelocity variations (as percentage) derived from noise analysis (grey dots) compared towater table level variations (dark grey line), as function of time before and during the pe-riod of landslide reactivation (marked by vertical grey bar). Arrows (1) and (2) mark thetimes of the beginning of velocity reduction and of the major velocity drop, respectively(modified from Mainsant et al., 2012).

195V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

mapped as function of frequency f and apparent slowness p (the inverseof apparent velocity) of the signals passed through the array alignment(see Figure 13a). On the p–f map coherent signals corresponding toRayleighwaves appear as trends of high power spectrumvalues. Pickingsome points along this trends, the corresponding combination of slow-ness p and frequency f provide a sampling of the Rayleigh wave phasevelocity dispersion curves: these can then be interpreted in terms ofVs velocity.

High spectral power values can be found in relation to energeticwave trains crossing the geophone array along different directions:the signal apparent velocity is given by the ratio between inter-geophone distance and time difference of signal passage. Apparentvelocity coincides with the real velocity only if waves propagate parallelto the geophone array: for waves having any other propagation direc-tion apparent velocity will be larger than the real one. Thus pickingp–f values along the lower boundary of the trend of high power spec-trum values, corresponding to the minimum velocity shown by coher-ent signals, provides the best approximation of the real velocitydispersion curve (see example in Figure 13a). In theory, this shouldassure that the picked dispersion curve corresponds to the Rayleighfundamental mode (which is the slowest). However, some authors(Xia et al., 2003; Luo et al., 2007) observed that at frequencies largerthan 17–18 Hz higher modes tend to be dominant: this is likely thereason why it is generally recommended to limit data interpretationto frequencies not larger than 2.5 times the sensor eigenfrequency.

We tested the application of ReMi technique to landslides in theCaramanico study area (Coccia et al., 2010), using a free software pack-age (DINVER: Wathelet, 2005) for the inversion of the dispersioncurves. Considering the difficulties in the array deployment posed by

irregular topography and lateral heterogeneities of soil material, weused arrays shorter than 100 m, which is the minimum length recom-mended in standard applications (Louie, 2001). Nonetheless, thanks tosome additional constraints from independent subsurface data, themethod proved capable of providing information about slope materialVs velocity down to a depth even a little larger than the commonlyassumed limit of half of the array length.

Measurements were also carried out deploying an L-shape arraywith two orthogonal branches, one of which approximately orientedalong the maximum slope direction. The dispersion curves obtained atsites outside the landslide body did not show directional differences,whereas, at a site on the landslide head, 10–20% lower velocities wereobserved along the maximum slope direction for all the investigatedfrequency range. This difference appears small, at the limit of uncertain-ty of the method, and influence of anisotropy of noise source distribu-tion cannot be excluded. Nonetheless, it is noteworthy that thedirectional difference is qualitatively consistent with that derived bycross-correlation analysis carried out along the alignment CAR5–CAR2–G12 (Section 5.2).

A significant outcome of this application was the recognition of por-tions of dispersion curves clearly belonging to different modes (seeFigure 13). However the repetition of measurements under differentenvironmental conditions shows that mode excitation can undergochanges, so that surveys conducted in different periods can sample dis-persion curves of different modes (Coccia et al., 2010). This poses someproblems inmode identification that can, however, be resolved througha careful comparative analysis of dispersion curves obtained from dataacquired at different times in nearby sites. Finally, the presence of amulti-modal signal in ambient noise has also a positive implication inthat, once modes are correctly identified, multi-modal curve inversionprovides a better resolution of model parameters (cf. Xia et al., 2000).

6. Discussion

The review of the recent literature on the dynamic response oflandslide-prone slopes and on the use of ambient noise analysis ininvestigations of seismic slope behaviour has raised important ques-tions that are discussed in the following. The issues of interest includei) factors controlling site response directivity, ii) effects of directionalresonance on slope stability and iii) contribution of ambient noise anal-ysis to improve slope hazard assessment.

6.1. Factors controlling site response directivity

The origins of the site directivity effects observed in landslide areasare still unclear in part because it is difficult (or impossible) to recognisea commonpredominant causative factor among all the case studies. Thispossibly reflects the existence of different mechanisms that can gener-ate directional resonance, so that somewhat different explanationscould be proposed case by case.

One factor that seems to play a clear role in determining azimuthalvariation of site response is topography. This is evident especially incase of elongated ridges, where a topographic effect reflects the anisot-ropy of the relief's shape. Below we describe a case from the ApennineMountains, where we could demonstrate the presence of topographiceffect with the aid of both accelerometer and ambient noise data.

The site of interest includes the accelerometer station of CastiglioneMesser Marino (CMM), belonging to the Italian National AccelerometerNetwork. The station is located about 10mbelow the top of a sandstonehill elongated for about 800 m in approximately N–S direction, beingabout 500mwide and having a local relief of 80m. The analysis of accel-erometer recordings of 9 seismic events pointed out a systematic pro-nounced directional maximum of shaking energy in the directiontransversal to the hill elongation, even though this shaking was notparticularly strong. Ambient noise measurements, analysed throughthe DHVPOR approach provided evidence of an approximately E–W

Fig. 13. Results of ReMi surveys conducted at site CAR2 repeating measurements at different times and with different configurations of the geophone array. a) map of the slowness–frequency (p–f) matrix obtained from the first measurement campaign and the results of the picking of points on p–f map, chosen to sample the Rayleigh wave velocity dispersioncurve (open squares); continuous curves represent isolines joining points whose combinations of p–f values correspond to fixed wavelengths (expressed in metres) annotated oneach isoline. One can notice that the series of picked points crosses in more points the same isoline: this implies that different velocity values are associated to the same wavelength,which is an evidence of the multi-modal nature of the resulting dispersion curve. b) Results of picking on p–f matrixes derived from different noise data acquisitions (distinguishedby different symbols) at the same site; the picking results are represented in terms of velocities as function of wavelength and these data clearly outline at least two distinct modesof Rayleigh waves interpreted as the fundamental and the first higher mode (modified from Coccia et al., 2010).

196 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

directivity of H/V peak. Furthermore, although a comparison of H/Vpeak mean values of the noise recordings acquired at the hill's baseand top showed a similar pattern, the presence of larger H/V ratio onthe hilltop (Figure 14) suggested a role of topographic amplification ingenerating the directivity.

A well known literature case of directional resonance is that of theTarzana hill (California), where the observed directions of major andsecondary amplifications were found, respectively, transversal andparallel to the hill elongation axis (Spudich et al., 1996). This led theauthors to infer a topographic control of the phenomenon.

However a purely topographic effect can hardly account for amplifi-cation factors larger than two,which, however, seem common in case ofdirectional resonance (Del Gaudio andWasowski, 2011). This points tothe geology, local structural characteristics, heterogeneities and anisot-ropies of rock mechanical properties, which could all have influence ondirectional variations of resonance.

An anisotropy of rock mechanical properties was invoked by Mooreet al. (2011) to explain the earlier cited case of the Randa rock slide: thestronger amplitude of ground motion affecting the unstable part of theslope in the maximum slope direction was attributed to the presenceof tension fractures oriented approximately perpendicular to the

displacement direction. These local structural features were the causeof an anisotropy of slopematerial deformability under the effect of seis-mic shaking.

A similar effect could be present also in landslides affecting softermaterials, considering an anisotropy of shear strength induced by grav-itational mass movement. For instance, at the interface between sub-stratum and surface layer, one can envision a directional variation ofwave velocity resulting in an increase of transmission coefficient forwaves polarised transversally to the prevailing fissuring direction.

However, the behaviour of slope materials under dynamic condi-tions is not controlled only by the mechanical effects of gravitationalmovements: it can depend also on structural features inherited fromtectonics and on 3D geometry of the near-surface geological bodies.The interaction of these different factors can explain the presence ofdirectivity diverging from maximum slope direction.

Recently Pilz et al. (2013) found evidence of a complex pattern ofresonance phenomena on the aforementioned landslide in Kyrgyzstan.Fieldmeasurements of H/V revealed the presence of a peak at frequencyof 2–2.5 Hz with an E–W directivity (approximately along the maxi-mum slope direction) throughout a 360 m long and 300 m wide land-slide body consisting of limestones and claystones. The common peak

a) CMM1 b) CMM2

0 2 4 6 8 10

mean H/V

c) DHVPOR CMM1 - CMM2

0

10

20

30

40

50

60

70

80

90

100

DH

VP

OR

0 2 4 6 8 10 12 14 16Frequency (Hz)

0

2

4

6

8

10

H/V

Rat

io

CMM1

CMM2

0 20 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

0 20 40 60 80 100 120 140 160 180

Azimut

02

46

810

1214

16Frequency (H

z)

1020

3040

5060

7080

9010

0

Per

cent

age

Fig. 14.Histograms of theDHVPORvalues calculated fromnoise recordings carried out at the site of CMMaccelerometer station, located 10mbelow the top of a hill (CMM1—a), and on thetop of the same hill (CMM2—b), and comparison of the results obtained along the direction of maximum DHVPOR (c) (CMM1 in red, CMM2 in blue); data representation as in Fig. 9.

197V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

frequency appeared independent from the landslide thickness and wasinterpreted as resonance effect of thewhole landslide body according toa vibration mode polarised along a direction characterised by weakermechanical constraints (corresponding to the slidingdirection). In addi-tion, the local occurrence of someminor peaks with higher frequenciesand variable directions was related to sub-units of the mass movementcharacterised by different mobility.

On the whole, the presence of a directional variability of ground mo-tion amplification reveals an anisotropy of geological body deformability,which can depend on morphological, structural and lithological factors(or their combination), acting at different frequencies. This implies thatthe direction of maximum amplification does not necessarily coincide

with that of maximum slope along which potential sliding can occur.Clearly, directional resonance will have an increased destabilising effecton slopes where these two directions are similar and the size of potentialfailures could be particularly sensitive to the site resonance frequencies.

6.2. Impact of slope resonance on slope stability

Another important question iswhether amplification effect can havea significant impact on slope stability andwhether directional variationsof the amplification can significantlymodify such an impact. Quantifica-tion of amplification factors on marginally stable slopes, based on acomparison of accelerometer with a nearby reference site, was obtained

198 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

only in a few case studies. These include the Utiku landslide (NewZealand—Garambois et al., 2010), Randa rockslope 2010 (Switerland—Moore et al., 2011) and the Caramanico hillslopes (Italy—Del Gaudioand Wasowski, 2007, 2011). The studies reported quite consistentvalues of spectral amplifications in a range of factors between 6 and10, with variations by a factor 2–3 between directional maximum andminimum. For the Italian site, Del Gaudio and Wasowski (2011) alsoanalysed amplification factors in terms of total shaking energyexpressed in terms of Arias intensity. They noted a considerable vari-ability of amplification factors at low magnitude events, which, athigher magnitudes tended to stabilise around a factor of 20 in directionofmaximumamplification (or around factor of 10 along a perpendiculardirection).

An estimate of the impact of amplification on slope destabilisationcan be obtained from empirical relations that link coseismic landslidedisplacement to shaking energy (e.g. Jibson, 2007). Such relations indi-cate that the logarithm of permanent displacement induced by shakingalong a sliding surface is proportional to the logarithm of shaking ener-gy according to a factor of 1.5 ormore (range in estimates reflects differ-ent datasets used). This implies that for an increase of Arias intensity bya factor of 10 or 20 (depending whether the direction of maximum orminimum amplification is considered), site amplification can cause amedian increase of displacement by a factor between 30 and 90. Thusthe impact in terms of increasing slope failure likelihood can be verysignificant.

6.3. Contribution of noise analysis to slope hazard estimates

At present the occurrence and the orientation of directional reso-nance cannot be determined from the examination of general slopecharacteristics. Therefore, noise analysis can represent a quick andcost-effective tool to provide insight on the strength demand for slopesto resist failure during seismic events. The data on slopes directional res-onance obtained from ambient noise measurements could help in abetter definition of a seismic scenario at regional scale, following ap-proaches like that proposed by Jibson et al. (2000). If specific accurateestimates of seismic shaking energy amplification factors are not avail-able, an indicative range of 10–20 can be suggested, the extreme valuesbeing adopted when potential sliding direction is perpendicular or par-allel, respectively, to maximum amplification direction derived fromnoise measurements.

For stability assessment at a single slope scale, noise analysis canprovide additional useful information concerning in particular majorresonance frequencies and amplification factors. With regard to reso-nance frequencies, in addition to a simple examination of azimuthalvariation of HVNR values, a more advanced analysis of azimuthal varia-tion of HVNR values can be needed when noise spectra show a complexpattern with multiple peaks and seasonal variability of their amplitude.In this context, methods aimed at extracting from the noise recordingthe contribution of Rayleighwaves can help to recognise noise spectrumproperties that more closely reflect site resonance frequencies.

Regarding amplification factors, the possibility of deriving them di-rectly from H/V ratio amplitude should be dealt with some caution.With reference to the complex site conditions often affectingmarginallystable slope, there are very few literature examples of a comparison be-tween HVNR data and spectral amplifications resulting from seismicevent recordings at the same site. The cases described by Burjáneket al. (2010) and Del Gaudio et al. (2013) suggest that an acceptableagreement between H/V ratios from noise recording and amplificationfactors relative to a reference site can be found in case of rock slopes.On the contrary, one case of a soil slope affected by landslide (DelGaudio et al., 2013) indicated that HVNR can lead to a considerableunderestimate of amplification (approximately by a factor of 2) at reso-nance frequencies, togetherwith a possible seasonal variability attribut-able to vertical component amplification. Thus in those cases the H/Vratio can be considered a lower boundary of the possible amplification

factor. A more reliable estimate can be obtained through an opportunechoice of themeasurement season: it seems better to conductmeasure-ments in periodswhen thewater table is at the highest level and, there-fore, P-wave velocity of the surface layer at its maximum.

However, for a slope-specific hazard assessment, a reliable evalua-tion of amplification may require numerical modelling of the slope re-sponse, using 1D or finite element approaches, according to whetherpotential slide surface depth is small or not in comparison to landslidesize. In addition to information onmaterial density, another basic ingre-dient required for numerical analysis is amodel of shear-wave velocitiesof slope material. This information could be retrieved directly from theambient noise data. An effective approach could consist of acquiringnoise records by using at least a couple of simultaneous synchronisedrecorders. In this way, together with the acquisition of data for HVNRanalysis, one can also obtain the data needed to derive Vs values ofthe slope material through correlation analysis.

7. Conclusions

Investigations of slope dynamic response to seismic shaking canbenefit from different techniques that exploit short term ambientnoise recordings and derive information on site resonance properties.The use of natural noise signals is attractive in studies on marginallystable slopes, since other approaches require more time and money(e.g. monitoring based on permanent in situ accelerometer stations)or pose problems in the deployment ofmore cumbersome instrumenta-tion (e.g. active geophysical survey methods).

The lack of control over signal source characteristics makes noisedata processing complex and interpretation difficult, especially asregards the uncertainties in noise source location and noise wavefieldnature (possibly including body waves and multimodal surfacewaves). Therefore, ambient noise analysis generally requires indepen-dent data to provide supplementary constraints for modelling andthereby reduce the range of solutions compatiblewith the experimentaldata. Ambient noise data can be effectively exploited in wide areaassessments of slope dynamic response, when some ground-truth isavailable (auxiliary data from active geophysics or geotechnical investi-gations) thusmaking cross-comparisons possible.Wide area acquisitionof noise data is feasible thanks to portable instruments like compacttromographs or geophones, which can also be employed at sites withdifficult access.

However, when investigating landslide prone slopes through ambi-ent noise analysis, ad hoc adaptations of data acquisition and processingprocedures may be needed to select the data containing the most rele-vant information. In particular, an improved capacity to identifyRayleigh wave trains in noise recording is desirable.

The information that can be relatively easily extracted from noiseanalysis is the presence and orientation of site directional resonance.This can be inferred from the search for persistent coherent site-specific directional maxima in H/V spectral ratios derived using theNakamura's technique and a procedure designed to reveal azimuthalvariations. More complex appears the identification of main resonancefrequencies, which can require i) the repetition of measurementsunder different environmental conditions, paying attention to ground-water conditions, and ii) the simultaneous acquisitions of noise data atnearby sites to distinguish site specific resonance properties fromsource controlled characteristics of the recorded wavefield.

Unfortunately, site amplification factors may not be confidentlyderived from ambient noise. However, noise data can be processedusing different techniques (H/V curve inversion, cross-correlation anal-ysis, ReMi) to model Vs velocity of slope material, which represents afundamental input in numerical modelling of site dynamic response.The application of these techniques on landslide prone slopes requiressome particular caution, taking into account possible effects of anisotro-py both in source noise spatial distribution and in slope materialproperties.

199V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

Acknowledgement

We thank the editors of this special issue and two anonymous re-viewers for their comments, which help to greatly improve this paper.

References

Aki, K., 1957. Space and time spectra of stationary stochastic waves, with special referenceto microtremors. Bull. Earthq. Res. Inst. 35, 415–457.

Albarello, D., Lunedei, E., 2009. Alternative interpretations of horizontal to vertical spec-tral ratios of ambient vibrations: new insights from theoretical modeling. Bull. Earth-quake Eng. 8, 519–534. http://dx.doi.org/10.1007/s10518-009-9110-0.

Arai, H., Tokimatsu, K., 2004. S-wave velocity profiling by inversion of microtremor H/Vspectrum. Bull. Seismol. Soc. Am. 94, 53–63.

Bard, P.-Y., 1999. Microtremormeasurements: a tool for site effect estimation? In: Irikura,K., Kudo, K., Okada, H., Sasatani, T. (Eds.), The Effects of Surface Geology on SeismicMotion. Balkema, Rotterdam, pp. 1251–1279.

Bard, P. Y., (coordinator) and The SESAME Team, 2004. Guidelines for the implementationof the H/V spectral ratio technique on ambient vibrations: measurements, processingand interpretation. SESAME European research project, WP12—Deliverable D23.12,http://sesame-fp5.obs.ujf-grenoble.fr/Papers/HV_User_Guidelines.pdf.

Bensen, G.D., Ritzwoller, M.H., Barmin, M.P., Levshin, A.L., Lin, F., Moschetti, M.P., Shapiro,N.M., Yang, Y., 2007. Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophys. J. Int. 169, 1239–1260.

Bonamassa, O., Vidale, J.E., 1991. Directional site resonances observed from aftershocks ofthe 18 October 1989 Loma Prieta earthquake. Bull. Seismol. Soc. Am. 81, 1945–1957.

Bonnefoy-Claudet, S., Cotton, F., Bard, P.-Y., 2006. The nature of seismic noise wavefieldand its implications for site effects studies—a literature review. Earth-Sci. Rev. 79,205–227.

Borcherdt, R.D., 1970. Effects of local geology on ground motion near San Francisco Bay.Bull. Seismol. Soc. Am. 60, 29–61.

Bourdeau, C., Havenith, H.-B., 2008. Site effects modeling applied to the slope affected bythe Suusamyr earthquake (Kyrgyzstan, 1992). Eng. Geol. 97, 126–145.

Bozzano, F., Lenti, L., Martino, M., Paciello, A., ScarasciaMugnozza, G., 2008. Self-excitationprocess due to local seismic amplification responsible for the reactivation of theSalcito landslide (Italy) on 31 October 2002. J. Geophys. Res. 113, B10312. http://dx.doi.org/10.1029/2007JB005309.

Bromirski, P.D., Duennebier, F.K., Stephen, R.A., 2005. Mid−oceanmicroseisms. Geochem.Geophys. Geosy. 6 (4), Q04009. http://dx.doi.org/10.1029/2004GC000768.

Burjánek, J., Gassner-Stamm, G., Poggi, V., Moore, J.R., Fäh, D., 2010. Ambient vibrationanalysis of an unstable mountain slope. Geophys. J. Int. 180, 820–828.

Castellaro, S., Mulargia, F., 2009. VS30 estimates using constrained H/V measurements.Bull. Seismol. Soc. Am. 99 (2A), 761–773.

Chigira, M., Yagi, H., 2006. Geological and geomorphological characteristics of landslidestriggered by the 2004 Mid Niigta prefecture earthquake in Japan. Eng. Geol. 82,202–221.

Chigira, M., Wang, W.-N., Furuya, T., Kamai, T., 2003. Geological causes and geomorpho-logical precursors of the Tsaoling landslide triggered by the 1999 Chi-Chi earthquake,Taiwan. Eng. Geol. 68, 259–273.

Coccia, S., Del Gaudio, V., Venisti, N., Wasowski, J., 2010. Application of RefractionMicrotremor (ReMi) technique for determination of 1-D shear wave velocity in alandslide area. J. Appl. Geophys. 71, 71–89. http://dx.doi.org/10.1016/j.jappgeo.2010.05.001.

Danneels, G., Bourdeau, C., Torgoev, I., Havenith, H.B., 2008. Geophysical investigation anddynamic modelling of unstable slopes: case-study of Kainama (Kyrgyzstan).Geophys. J. Int. 175, 17–34.

Del Gaudio, V., Wasowski, J., 2007. Directivity of slope dynamic response to seismic shak-ing. Geophys. Res. Lett. 34, L12301. http://dx.doi.org/10.1029/GL029842.

Del Gaudio, V., Wasowski, J., 2011. Advances and problems in understanding the seismicresponse of potentially unstable slopes. Eng. Geol. 122, 73–83.

Del Gaudio, V., Coccia, S., Wasowski, J., Gallipoli, M.R., Mucciarelli, M., 2008. Detection ofdirectivity in seismic site response from microtremor spectral analysis. Nat. HazardsEarth Syst. Sci. 8, 751–762.

Del Gaudio, V., Wasowski, J., Muscillo, S., 2013. New developments in ambient noise anal-ysis to characterise the seismic response of landslide prone slopes. Nat. Hazards EarthSyst. Sci. 13 (2075–2087), 2013. http://dx.doi.org/10.5194/nhess-13-2075-2013.

Dziewonski, A.M., Bloch, S., Landisman, M., 1969. A technique for the analysis of transientseismic signals. Bull. Seismol. Soc. Am. 59, 427–444.

Ekström, G., Abers, G.A., Webb, S.C., 2009. Determination of surface-wave phase velocitiesacross USArray from noise and Aki's spectral formulation. Geophys. Res. Lett. 36,L18301. http://dx.doi.org/10.1029/2009GL039131.

Fäh, D., Kind, F., Giardini, D., 2001. A theoretical investigation of average H/V ratios.Geophys. J. Int. 145, 535–549.

Fan, X., van Westen, C.J., Korup, O., Gorum, T., Xu, X., Fuchu Dai, F., Huang, R., Wang, G.,2012. Transient water and sediment storage of the decaying landslide dams inducedby the 2008 Wenchuan earthquake, China. Geomorphology 171–172, 58–68.

Gallipoli, M.R., Mucciarelli, M., 2007. Effetti direzionali in registrazioni sismometriche inaree in frana e bordi di bacino. XXVI GNGTS Conference, Rome, 13–15 November2007 (http://www2.ogs.trieste.it/gngts/gngts/convegniprecedenti/2007/presentazioni/2_22/2_22_14_Gallipoli_frane.pdf).

Gallipoli, M.R., Lapenna, V., Lorenzo, P., Mucciarelli, M., Perrone, A., Piscitelli, S., Sdao, F.,2000. Comparison of geological and geophysical prospecting techniques in thestudy of a landslide in southern Italy. Eur. J. Environ. Eng. Geophys. 4, 117–128.

Garambois, S., Quintero, A., Massey, C., Voisin, C., 2010. Azimuthal and thickness variabil-ities of seismic site effect response of the Utiku landslide (North Island, New-Zealand). EGU General Assembly 2010, Vienna, 2–7 May, 2010. Geophys. Res. Abstr.12, EGU2010–EGU2430.

Gorum, T., Fan, X., vanWesten, C.J., Huang, R., Xu, Q., Tang, C., Wang, G., 2011. Distributionpattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuanearthquake. Geomorphology 133, 152–167.

Haghshenas, E., Bard, P.-Y., Theodulis, N., SESAMEWP04 Team, 2008. Empirical evaluationof microtremor H/V spectral ratio. Bull. Earthq. Eng. 6, 75–108.

Harp, E.L., Jibson, R.W., 2002. Anomalous concentrations of seismically triggered rock fallsin Paicoma Canyon: are they caused by highly susceptible slopes or local amplifica-tion of seismic shaking? Bull. Seismol. Soc. Am. 92, 3180–3189.

Harp, E.L., Wilson, R.C., Wieczorek, G.F., 1981. Landslides from the February 4, 1976,Guatemala earthquake. USGS Professional Paper 1204-A (40 pp.).

Havenith, H.B., Jongmans, D., Faccioli, E., Abdrakhmatov, K., Bard, P.-Y., 2002. Site effectanalysis around the seismically induced Ananevo rockslide, Kyrgyzstan. Bull. Seismol.Soc. Am. 92, 3190–3209.

Herrmann, R.B. (Ed.), 2010. An overview of synthetic seismogram computation. Comput-er Programs in Seismology. Saint Louis University (Version 3.30 [electronic], http://www.eas.slu.edu/eqc/eqccps.html).

Hudson,M.B., Idriss, I.M., Beikae,M., 2003.QUAD4M, a computer programtoevaluate the seis-mic response of soil structures using finite element procedures and incorporating a com-pliant base. Pacific Earthquake Engineering Research Center, University of California,Berkeley, California, (http://nisee.berkeley.edu/documents/SW/QUAD4M.pdf).

Jibson, R.W., 2007. Regression models for estimating coseismic landslide displacement.Eng. Geol. 91, 209–218.

Jibson, R.W., 2011. Methods for assessing the stability of slopes during earthquakes—aretrospective. Eng. Geol. 122, 43–50.

Jibson, R.W., Harp, E.L., Michael, J.A., 2000. A method for producing digital probabilisticseismic landslide hazard maps. Eng. Geol. 58, 271–289.

Jongmans, D., Bievre, G., Renalier, F., Schwartz, S., Beaurez, N., Orengo, Y., 2009. Geophys-ical investigation of a large landslide in glaciolacustrine clays in the Triéves area(French Alps). Eng. Geol. 109, 45–56.

Kottke, A.R., Rathje, E.M., 2008. Technical manual for STRATA. Pacific Earthquake Engi-neering Research Center Report 2008/10. University of California, Berkeley, California.

Lenti, L., Martino, S., 2012. The interaction of seismic waves with step-like slopes and itsinfluence on landslide movements. Eng. Geol. 126, 19–36. http://dx.doi.org/10.1016/j.enggeo. 2011.12.002.

Lermo, J., Chávez-García, F.J., 1994. Are microtremors useful in site response evaluation?Bull. Seismol. Soc. Am. 84, 1350–1364.

Levshin, A.L., Pisarenko, V.F., Pogrebinsky, G.A., 1972. On a frequency–time analysis ofoscillations. Ann. Geophys. 28, 211–218.

Ling, S., Okada, H., 1993. An extended use of the spatial autocorrelation method forthe estimation of structure using microtremors. Proc. of the 89th SEGJ Conference,Nagoya, Japan, 12–14 October 1993. Society of Exploration Geophysicists of Japan,pp. 44–48 (in Japanese).

Lobkis, O.I., Weaver, R.L., 2001. On the emergence of the Greens function in the correla-tions of a diffuse field. J. Acoust. Soc. Am. 110, 3011–3017.

Longuet-Higgins, M.S., 1950. A theory of the origin of microseisms. Philos. Trans. R. Soc. AMath. Phys. Sci. 243 (857), 1–35.

Louie, J.N., 2001. Faster, better: shear-wave velocity to 100 meters depth from refractionmicrotremor arrays. Bull. Seismol. Soc. Am. 91, 347–364.

Luo, Y., Xia, J., Liu, J., Liu, Q., Xu, S., 2007. Joint inversion of high-frequency surface waveswith fundamental and higher modes. J. Appl. Geophys. 62, 375–384.

Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., Jaboyedoff, M., 2012.Ambient seismic noise monitoring of a clay landslide: toward failure prediction.J. Geophys. Res. 117, F01030. http://dx.doi.org/10.1029/2011JF002159.

Méric, O., Garambois, S., Malet, J.P., Cadet, H., Gueguen, P., Jongmans, D., 2007. Seismicnoise-based methods for soft-rock landslide characterization. Bull. Soc. Geol. Fr. 178,137–148.

Meunier, P., Hovius, N., Haines, J.A., 2008. Topographic site effects and the location ofearthquake induced landslides. Earth Planet. Sci. Lett. 275, 221–232.

Moore, J.R., Gischig, V., Burjanek, J., Loew, S., Fäh, D., 2011. Site effects in unstable rockslopes: dynamic behavior of the Randa instability (Switzerland). Bull. Seismol. Soc.Am. 101 (6), 3110–3116.

Mulargia, F., 2012. The seismic noise wavefield is not diffuse. J. Acoust. Soc. Am. 131,2853–2858.

Mulargia, F., Castellaro, S., 2010. Nondiffuse elastic and anelastic passive imaging. J. Acoust.Soc. Am. 127, 1391–1396.

Nakamura, Y., 1989. A method for dynamic characteristics estimation of subsurface usingmicrotremor on the ground surface. Q. Rep. Railw. Tech. Res. Inst. 30, 25–33.

Nogoshi, M., Igarashi, T., 1971. On the amplitude characteristics of microtremor (part 2)(in Japanese with English abstract). J. Seismol. Soc. Jpn. 24, 26–40.

Nunziata, C., De Nisco, G., Panza, G.F., 2009. S-waves profiles from noise cross correlationat small scale. Eng. Geol. 105, 161–170.

Ohori, M., Nobata, A., Wakamatsu, K., 2002. A comparison of ESAC and FK methods ofestimating phase velocity using arbitrarily shaped microtremor arrays. Bull. Seismol.Soc. Am. 92, 2323–2332.

Peterson, J., 1993. Observation and modeling of background seismic noise. US Geol. Surv.Open-File Rept. 93-322, Albuquerque.

Pilz, M., Parolai, S., Bindi, D., Saponaro, A., Abdybachaev, U., 2013. Combining seismicnoise techniques for landslide characterization. Pure Appl. Geophys. http://dx.doi.org/10.1007/s00024-013-0733-3.

Poggi, V., Fäh, D., Burjánek, J., Giardini, D., 2012. The use of Rayleigh-wave ellipticity forsite-specific hazard assessment and microzonation: application to the city of Lucerne,Switzerland. Geophys. J. Int. 188, 1154–1172.

200 V. Del Gaudio et al. / Engineering Geology 182 (2014) 182–200

Rial, J.A., 1996. The anomalous seismic response of the ground at the Tarzana Hill site dur-ing the Northridge 1994 Southern California earthquake: a resonant, sliding block?Bull. Seismol. Soc. Am. 86, 1714–1723.

Sassa, K., Fukuoka, H., Wang, F., Wang, G., Okada, K., Marui, H., 2006. Landslide disasterstriggered by the 2004 Mid-Niigata Prefecture earthquake in Japan. Ann. DisasterPrev. Res. Inst., Kyoto Univ. 49, 119–136.

Schimmel, M., Stutzmann, E., Ardhuin, F., Gallart, J., 2011. Polarized Earth's ambientmicroseismic noise. Geochem. Geophys. Geosyst. 12 (7), Q07014. http://dx.doi.org/10.1029/2011GC003661.

Sepúlveda, S.A., Murphy, W., Jibson, R.W., Petley, D.N., 2005. Seismically induced rockslope failures resulting from topographic amplification of strong ground motions:the case of Pacoima Canyon California. Eng. Geol. 80, 336–348.

Shapiro, N.M., Campillo, M., 2004. Emergence of broadband Rayleigh waves from correla-tions of the ambient seismic noise. Geophys. Res. Lett. 31, L07614. http://dx.doi.org/10.1029/2004GL019491.

Spudich, P., Hellweg, M., Lee, W.H.K., 1996. Directional topographic site response atTarzana observed in aftershocks of the 1994 Northridge, California, earthquake:implications for mainshock motions. Bull. Seismol. Soc. Am. 86 (1B), S193–S208.

Tang, C., Zhu, J., Xin, Q., Ding, J., 2011. Landslides induced by the Wenchuan earthquakeand the subsequent strong rainfall event: a case study in the Beichuan area ofChina. Eng. Geol. 122, 22–33.

Torgoev, A., Lamair, L., Torgoev, I., Havenith, H.-B., 2013. A review of recent case studies oflandslides investigated in the Tien Shan Using microseismic and other geophysicalmethods. In: Ugai, K., Yagi, H., Wakai, A. (Eds.), Earthquake-Induced Landslides,

Proceedings of the International Symposium on Earthquake-Induced Landslides,Kiryu (Japan), 7–9 November, 2012. Springer. ISBN: 978-3-642-32237-2, pp.285–294. http://dx.doi.org/10.1007/978-3-642-32238-9_29.

Tuan, T.T., Scherbaum, F., Malischewsky, P.G., 2011. On the relationship of peaks andtroughs of the ellipticity (H/V) of Rayleigh waves and the transmission response ofsingle layer over half-space models. Geophys. J. Int. 184, 793–800.

Wasowski, J., Del Gaudio, V., 2000. Evaluating seismically-induced mass movement haz-ard in Caramanico Terme (Italy). Eng. Geol. 58 (3/4), 291–311.

Wasowski, J., Lee, C.T., Keefer, D., 2011. Toward the next generation of research on earth-quake induced landslides: current issues and future challenges. Eng. Geol. 122 (1–8),2011. http://dx.doi.org/10.1016/j.enggeo.2011.06.001.

Wathelet, M., 2005. Array recordings of ambient vibrations: surface-wave inversion. (PhDthesis) Université de Liège, Belgium.

Xia, J., Miller, R.D., Park, C.B., 2000. Advantage of calculating shear-wave velocity from sur-face waves with higher mode. Technical programwith biographies, SEG, 70th AnnualMeeting, Calgary, Canada, pp. 1295–1298.

Xia, J., Miller, R.D., Park, C.B., Tian, G., 2003. Inversion of high frequency surface waveswith fundamental and higher modes. J. Appl. Geophys. 52, 45–57.

Xu, Z., Schwartz, S.Y., Lay, T., 1996. Seismic wave-field observations at a dense, small ap-erture array located on a landslide in the Santa Cruz Mountains, California. Bull.Seismol. Soc. Am. 86, 655–669.