Geophysical Journal International

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1 Geophysical Journal International Geophys. J. Int. (2010) 182, doi: /j X x A detailed study of the site effects in the volcanic area of Campi Flegrei using empirical approaches Anna Tramelli, Danilo Galluzzo, Edoardo Del Pezzo and Mauro A. Di Vito Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli Osservatorio Vesuviano, Via Diocleziano 328, 80124, Napoli, Italy. anna.tramelli@ov.ingv.it Accepted 2010 May 20. Received 2010 May 5; in original form 2009 July 23 1 INTRODUCTION Determination of region-specific ground motion is of great significance for geologically heterogeneous and highly populated areas. As local site effects can have strong influences on both amplitude and duration of recorded ground shaking, a knowledge of the sitetransfer functions (STFs) is fundamental to the hazard definition of an area and for the design of seismic safety facilities (Edwards et al. 2008). Campi Flegrei is a densely populated volcanic area in the south of Italy. The seismic hazard of this area is mainly generated by the occurrence of tectonic earthquakes in the Appenines (e.g. M = 6.9, 1980 November 23; Nunziata 2004) and by local seismicity associated with the bradyseismic phenomena (Aster et al. 1989). Campi Flegrei is a nested caldera that is characterized by a highly complex geological and topographical structure (Di Vito et al. 1999); this complexity can influence the behaviour of seismic waves near the surface (Borcherdt 1970). As was shown for the 1980 November 23, earthquake (Pantosti & Valensise 1993), large SUMMARY Campi Flegrei is a highly populated active caldera in the south of Italy. Several hundred thousand people live within this area, which is characterized by seismicity and ground deformation episodes, known as bradyseism. For this reason, this area falls into a high-risk category and thus the Italian Civil Defence requires a detailed site-effect estimation. To determine the local amplification of the seismic waves for a high number of sites, we have analysed the seismic recordings of three seismic networks that have been deployed in the Campi Flegrei area over different time periods. The first network was deployed during the bradyseismic crisis of We selected 22 of the highest magnitude earthquakes that were recorded during this crisis. An additional 22 seismic events were selected from those recorded by the mobile seismic network that has been in operation in the Campi Flegrei area since The third data set comprises noise recorded by 34 seismic stations that were deployed during the active SERAPIS experiment in 2001 September. The generalized inversion technique and the H/V spectral ratio method were applied to the S waves and coda waves of the earthquakes recorded by the first two seismic networks, to determine the site-transfer functions of the recording stations. The seismic noise recorded by the third network was analysed using the Nakamura s technique. The results show that the high topographical and geological heterogeneity of the sites located inside the caldera has an important influence on the seismic-wave amplification. Consequently, the site-transfer functions can be different even at sites close to each other. The transfer functions of the sites located outside the caldera are much more regular, apparently due to the more regular topography and geology. Key words: Earthquake ground motions; Site effects differences in the damage to buildings were seen in areas that were relatively close to each other, which were not justified by building construction quality alone (Nunziata 2004). The high volcanic risk associated with possible unrest of the Campi Flegrei area stimulated the Italian Civil Defence (the DPC) to ask for detailed site-effect analyses that can be used for precise estimations of the ground motion caused by local earthquakes. There are several techniques to estimate such site responses, the most common of which is the standard spectral ratio (SSR) method, which was developed by Borcherdt (1970). This method works by dividing the spectrum observed at the analysed site by that observed at a reference site that is characterized by negligible site amplification, and it is based on the assumption that the two stations are close to each other in comparison to the source-to-site distance. This SSR method was recast by Andrews (1986) into the generalized inversion technique (GIT), by simultaneous solving of the data of multiply recorded events. This GIT method allows the determination of the source, site and path effects simultaneously. For the GIT, a reference site is necessary to constrain the solution: as a reference, the network average (Phillips & Aki 1986; GJI Seismology C 2010 The Authors 1073

2 1074 A. Tramelli et al. Galluzzo et al. 2009), a bedrock station (Hartzell 1992), or the average over several rock stations (Bonilla et al. 1997) can be chosen. A non-reference-site-dependent technique was then introduced by Nakamura (1989), using the noise spectral ratio between the horizontal and vertical components. Lermo & Chavez-Garcia (1993) calculated the direct S-wave horizontal-to-vertical spectral ratios (HVSRs). These two procedures assume that the shear-wave vertical component recorded at the surface is not influenced by the local site conditions. All of the methods described above were extensively analysed and compared by Field & Jacob (1995). The SSR and GIT methods are based on similar hypothesis, but the GIT approach is more efficient when not all of the events are recorded at some sites. Furthermore, the GIT method allows the estimation of useful information about the source spectrum of the selected earthquakes. The HVSR approach is particularly fast and can measure the fundamental frequency, not the peak amplitude, especially when the topography is complex (Lee et al. 2009) or when the site is located inside a basin (Bindi et al. 2009). Figure 1. The Campi Flegrei area showing the stations belonging to the three networks: the Wisconsin network (WN; blue triangles), the mobile seismic network (MSN; violet triangles) and the SERAPIS network (SN; black triangles). The epicentres of the earthquakes used in the present study are shown by the stars: blue for the Wisconsin network and violet for the mobile seismic network. The UTM coordinates are in metres. Table 1. Locations of the stations belonging to the Wisconsin network (top) and the mobile seismic network (bottom). Name Site Latitude (N) Longitude (E) Elevation (m a.s.l.) W03 Astroni W04 Mt. Nuovo W05 Camaldoli W10 Capo Miseno W12 Solfatara W13 Castello Baia W14 Nisida W15 Mt.Spina W17 da Mario W20 Mt. S. Angelo W21 Fondi Cigliano AMS2 Mt. Spina ASB2 Astroni BGNG Bagnoli CSI Pozzuoli OMN2 Mt. Nuovo TAGG Terme Agnano

3 Site effects at Campi Flegrei 1075 The SSR method was applied in the area of Campi Flegrei using the S waves and coda waves of the local earthquakes recorded during the bradyseismic crisis of (Del Pezzo et al. 1993). In this analysis the reference station was chosen as the one that recorded the highest number of earthquakes. This choice was justified by the absence of a bedrock site. Other considerations concerning the seismic ground motion of the Campi Flegrei area were performed by Nunziata (2007) for the section that is included in the Neapolitanian municipality. Nunziata et al. (2004) estimated the seismic ground motion in the urban area of Naples due to the 1980 Irpinia earthquake, and correlated this with the geological setting of each district. Nunziata (2007) compared the same geological setting with the STFs calculated using seismic noise measurements. Petrosino et al. (2008a) arranged a field experiment in the Solfatara crater (in the middle of the Campi Flegrei area) to record the seismic noise for analysis according to the Nakamura s method. All of these studies carried out for the Campi Flegrei area have revealed the great complexity of the site, which is due to its geological and topographical properties. Here, we aimed to define a complete microzonation of the area using most of the seismic data that have been collected, starting from the local earthquakes that occurred during the bradyseismic crisis of We applied both the HVSR and the GIT methods, and we have compared these results with each other and with previously reported results. This analysis was completed by applying HVSR to the seismic-noise data recorded during the SEismic Reflection/Refraction Acquisition Project for Imaging complex volcanic Structures (SERAPIS) experiment, which was performed in 2001 September (Judenherc & Zollo 2005). The final results are compared to the theoretical HVSR STFs that were obtained for the sites where the detailed geological structure is available. Table 2. Origin time (YYYYMMDD/hhmm/ss.ss) and localization of the seismic events used for the present analysis. Event Origin time Latitude (N) Longitude (E) Depth (km b.s.l.) /0049/ /0214/ /0422/ /0023/ /0107/ /0548/ /1631/ /0039/ /1610/ /1709/ /1027/ /1646/ /1719/ /2352/ /2319/ /1214/ /0528/ /1908/ /2121/ /0209/ /0730/ /2145/ /0047/ /2020/ /0112/ /2310/ /1650/ /0605/ /0806/ /0807/ /0809/ /0811/ /2218/ /0342/ /0453/ /1616/ /1758/ /2225/ /2242/ /2353/ /1906/ /1910/ /1917/ /1920/

4 1076 A. Tramelli et al. 2 THE DATA SET To obtain a detailed site-effect estimation of the Campi Flegrei area, the data recorded by most of the seismic stations operating in the area since 1982 were collected and analysed. The data set used for this site-effect evaluation in the Campi Flegrei area can be divided into the following three groups. (1) Data recorded by the seismic network of the University of Wisconsin, Wisconsin, USA (the Wisconsin network; WN). (2) Data recorded by the mobile seismic network of the National Institute of Geophysics and Volcanology (INGV), Vesuvian Observatory (the mobile seismic network; MSN). (3) Data collected during the active SERAPIS experiment (the SERAPIS network; SN). 2.1 The Wisconsin network The data belonging to the WN group were recorded during the bradyseismic crisis at Campi Flegrei. This crisis was characterized by a total ground uplift of more than 1.7 m (Aster et al. 1989). During this crisis, thousands of shallow (depth <3 km), low magnitude (M d 4.0) earthquakes were recorded. The seismic activity became more intense during 1984 March and April, with a large swarm on 1984 April 1. The seismic events were recorded by the WN that was deployed in the Campi Flegrei area by the University of Wisconsin, USA. This was composed of short-period, threecomponent, high-dynamic-range (106 db) digital stations, with a local recording system. In Fig. 1, the station positions are indicated by blue triangles, and Table 1 gives the station coordinates. The data were recorded with different sampling rates, which were sometimes changed during the field surveys. To provide a uniform Table 3. Seismic data set used in this study. Event AMS2 ASB2 BGNG CSI TAGG OMN2/W04 W03 W05 W10 W12 W13 W14 W15 W17 W20 W21 01 x x x 02 x x x 03 x x 04 x x x x 05 x x x x 06 x x x x 07 x x x x 08 x x x x 09 x x x x x x 10 x x x x x 11 x x x x x 12 x x x x x x x 13 x x x x x x x x 14 x x x x x x x x 15 x x x x x x x x 16 x x x x x x x x 17 x x x x x x x x 18 x x x x x x x x x 19 x x x x x x x 20 x x x x x x x x x x 21 x x x x x x x x 22 x x x x x x x x x 23 x x x x 24 x x x x x 25 x x x x 26 x x x 27 x x x 28 x x x x 29 x x x 30 x x x x 31 x x x x 32 x x x 33 x x x x 34 x x x x 35 x x x 36 x x x 37 x x x 38 x x x x x 39 x x x x x x 40 x x x x x 41 x x x 42 x x x x 43 x x x x 44 x x x x

5 Site effects at Campi Flegrei 1077 data set, we resampled all of the records at 100 sps, the lowest sampling rate. We select 22 earthquakes (see Table 2) with high signal-to-noise ratios that were uniformly spread in the area. In Fig. 1, the epicentres of these selected earthquakes are shown with blue stars, and Table 3 indicates the recording stations. 2.2 The mobile seismic network The second data set comprises 22 earthquakes that were recorded by the MSN, which occurred during the years 2006 and 2008 (see Table 2). The magnitudes of these earthquakes were relatively low (M d 2), and their epicentre locations were limited to a narrow area (Fig. 1, violet stars). We selected six high dynamics (24 bit) digital stations of the MSN located in the Campi Flegrei area, five of which were equipped with broad-band sensors, and one with a short-period sensor (CSI). Fig. 1 shows the station locations (violet triangles), and Table 1 gives their coordinates. The timing signal was synchronized using external GPS, and the sampling rates were set to 100 or 125 sps. As before, all of the signals were resampled at 100 sps. Most of these stations are still working in the Campi Flegrei area. 2.3 The SERAPIS network The SERAPIS temporary network was deployed in the area of Campi Flegrei during the SERAPIS experiment that was carried out in 2001 September. The seismometer network covered an area of more than km 2, which included the bays of Naples and Pozzuoli (Judenherc & Zollo 2005). For two weeks, the ship Le Nadir produced source shots with an air gun inside Pozzuoli Bay, following a grid pattern along N S and E W lines. The onland acquisition array was designed to provide very high data density, and comprised 18 vertical and 66 three-component sensors that continuously recorded during the experiment. We selected 33 three-component stations located in the Campi Flegrei area (see Table 4), and we have analysed the seismic noise recorded during the experiment; Fig. 1 shows the stations used for this site analysis (black triangles). As the signal-to-noise ratios of the shots were low, we decided to analyse the whole seismograms, including the transient signals (Parolai & Galiana-Merino 2006). The data set of the SERAPIS network selected for this analysis comprises 100 min of recordings of the seismic noise (and shots) for each of the stations selected. 2.4 Earthquake location The seismic events used for the STF analysis (from the WN and MSN groups) were picked manually for both the P and S-wave first arrivals. The events recorded by the MSN were of lower magnitude than those recorded by the WN. Sometimes the S-wave onset was difficult to pick; in these cases, we chose an S-wave onset at 1.7 times the P-wave traveltime. We selected earthquakes recorded by at least three of the stations, with high signal-to-noise ratios. The locations were obtained using the NonLinLoc programme (Lomax et al. 2000), which determines the hypocentre location within a 3-D grid using a systematic grid search. The velocity model developed by Judenherc & Zollo (2005) was used. Fig. 1 shows the locations of the earthquakes (WN, blue stars; MSN, violet stars). Table 2 gives the hypocentre coordinates. Table 4. Locations of the stations belonging to the SERAPIS network. Name Latitude (N) Longitude (E) Elevation (m a.s.l.) AER ANF BAI BAM CAM CAS CER CGR CHI CSM DIC DMP EI FOR GRM HC IT IT LU LU NAP NIS OAS OST PCU PPB SCO SFT SIT SNG STH VIL VLR ANALYSIS METHODS AND RESULTS We evaluated the site amplification with two different empirical techniques, and then compared the results. We applied both the HVSR and GIT methods to the S waves and coda waves of the earthquakes recorded by the WN and MSN. The Nakamura s technique was applied to the seismic noise recorded by the SN. 3.1 Earthquake analysis The HVSRs of the earthquakes were estimated by calculating the logarithmic arithmetical averages of the two horizontal spectra, divided by the spectrum of the vertical component (Bonilla et al. 1997). The HVSRs were evaluated for the S and coda waves separately, smoothing the spectral ratios with a rectangular sliding window (arithmetical mean over a five-point sliding window 20 per cent overlapping equivalent to 0.33 Hz). In Fig. 2, the S-wave results are shown (black lines) with their errors (black dashed lines), along with the results obtained for the coda waves (grey lines). These two curves show a very good match. The application of the GIT method needed the matching of the WN and MSN data sets. These two networks have no temporal continuity, and there were no contemporary earthquakes recorded by the two networks: the WN was operating during the bradyseismic crisis ( ) and the MSN has been operating since The union of the two data sets allows a common reference site to be used (mean of the sites). The separate inversion of the two data

6 1078 A. Tramelli et al. Figure 2. Site amplification functions calculated using the HVSR method applied to the S waves (black) and coda waves (grey) for the sites of the Wisconsin network and the mobile seismic network stations. The estimated errors (1σ ) associated with the S waves are shown by the dashed lines. sets would not allow the results to be compared, as neither of the data sets has a bedrock reference site. We connected the data sets through two stations that were located close to each other and that showed almost identical STFs. Fig. 3 shows the separate estimates of the H/V ratios for the W04 and OMN2 stations. These results show that the OMN2 and W04 stations have similar HVSRs within an error of one standard deviation, and they support the use of the OMN2 and W04 stations to connect these two data sets: indeed, the OMN2 and W04 stations were considered as a unique station in the analysis. The GIT (Tsujiura 1978; Andrews 1986; Hartzell 1992; Lachet et al. 1996; Bonilla et al. 1997; Parolai et al. 2001; Drouet et al. 2008) was applied to the matched data set, for both the S waves and the coda waves recorded by the WN and the MSN groups. For the S-wave analysis, we considered a time window of 3 s, starting 0.1 s before the S-wave arrival time; for the coda waves,

7 Site effects at Campi Flegrei 1079 Figure 3. Site amplification functions calculated using the HVSR method applied to the S waves recorded by stations W04 (black) and OMN2 (grey) (average: lines; ±σ : dashed lines). we also considered a time window of 3 s, but starting at a lapse time of 8 s. The choice of 3 s was a compromise in order to select a direct S-wave train with a minimum influence of scattered and surface waves; this choice was also used for the coda waves, for the sake of uniformity. The lack of hard-rock sites led us to consider the average spectrum between all of the investigated sites as the reference spectrum (Phillips & Aki 1986). By applying the GIT, we corrected the amplitude spectra for geometrical spreading and for the quality factor Q of the S waves or of the coda waves, depending on the window of signal analysed (as shown by eq. 1 for the S waves). The correction equations that were used are well explained in Galluzzo et al. (2009). For the S waves we defined the corrected spectral amplitude as A ij ( f ) A ij ( f ) = π R ij, (1) f Q R ij e S ( f ) v I j where A ij ( f ) is the seismogram spectral amplitude of event i at station j, R ij is the hypocentre-to-station distance, and accounts for the geometrical spreading, v is the mean S-wave velocity along the wave path (taken by the tomography of Judenherc & Zollo (2005), v = 1.8 km s 1 ), I j is the seismometer transfer function, and Q S is the frequency-dependent S-wave quality factor, as described by Petrosino et al. (2008b) [Q S ( f ) = 21 f 0.6 ]. The quality factor used for the coda waves was 180, as the mean value of those of Del Pezzo et al. (1993). The linear problem solved was ln[a ij ( f )] = ln[k i ( f )] + ln[s j ( f )], (2) where K i ( f ) is the source term, and S j ( f ) is the site term. Considering all of the earthquake-station pairs, as shown in Table 3, this problem can be represented as: Gm = d, for each frequency windows analysed. The singular value decomposition technique was used to find the values of the site and source terms (Menke 1989). Again, we show here the results obtained for the GIT applied to the S-wave window and compare these with the results obtained for the coda waves (Fig. 4). The standard deviation (σ )onln(a ij )is estimated by propagating the uncertainties on the parameters (R ij and Q S ) of the eq. (1). The uncertainties in the data are taken as: 200 m for the hypocentre-to-station distance (R ij ),and10percentof the value for the quality factor (Q S ). The frequency and the velocity are considered with no error. The standard deviations estimated in this way are mapped to the errors in the model parameters by the equation: σ m = G 1 σ 2 [G 1 ] T. σ m isshowninfig.4(dashed lines). An estimation of the stability of the solution was also made using the bootstrap method (Moore & McCabe 2006); the resolution process (through the singular value decomposition method) of the system of eq. (2) was applied 700 times, deleting 10 per cent of the rows of the matrix G each time. Each solution is shown in Fig. 4 (grey lines), along with the mean of the bootstrap solutions (Fig. 4, black lines): this overlaps perfectly with the solution of the complete system. By applying the bootstrap method, we made sure that we did not delete the row of the G matrix that contains the constraint that the network average STF equals 1. From Fig. 4, it can be seen that the results are stable for almost all of the stations (with the exception of stations W10 and W21). To better understand the differences between the STFs obtained with the GIT and HVSR methods, we also determined the spectrum of the vertical component of the S waves using the GIT technique applied to the vertical S-wave windows. The results obtained are showninfig.5. Comparing the STFs calculated with the GIT method (Fig. 4) with those obtained with the HVSR method (Fig. 2), it can be seen that there is a global good agreement in the shape of the transfer functions. We cannot compare the two amplifications, as the HVSR method is useful for the estimation of the fundamental frequencies of the site, but not the amplification. For some stations, the STFs calculated with the GIT show a vertical shift that can be explained by the use of the reference condition. The data show the following main characteristics: (i) The peak (amplitude 2) between 3 Hz and 10 Hz that is evident for the STF of station W03 calculated with the HVSR is not seen in the STF calculated with the GIT; on the other hand, the vertical component spectrum of W03 shows a high de-amplification in the frequency band of 3 Hz to 10 Hz; (ii) Station W10 shows a different STF pattern, with three peaks at 5 Hz, 10 Hz and 20 Hz for the STF calculated with the HVSR method, and only two peaks at 10 Hz and 20 Hz for the STF calculated with the GIT method; again, its vertical component spectrum shows de-amplification between 4 Hz and 10 Hz; (iii) Station ASB2 shows two different STF curves for frequency >5 Hz using the two methods; its vertical component spectrum shows amplification in a broad window of 2 11 Hz. 3.2 Noise analysis The analysis of the SN data set was performed by the application of the HVSR technique (Nakamura 1989) to all of the seismic recordings (noise and shots) using 100-min windows of seismic signal for each station. We estimated the H/V ratio by averaging the two horizontal spectra evaluated in a 5-s window, and then dividing this by the vertical spectrum evaluated in the same window. We calculated the H/V ratios for each moving window with an overlap of 30 per cent. The same procedure was performed for each station, and the results are shown in Figs 6 and 7. The errors were estimated as the standard deviations of the means performed over the windows (Figs 6 and 7, dashed lines). In analysing the STFs obtained using the HVSR method applied to the SN (Figs 6 and 7), we noted that only a few peaks (amplitude 2) were evident, and that the curves looked quite smoothed. We decided to group the sites into three

8 1080 A. Tramelli et al. Figure 4. Site amplification functions calculated using the GIT method applied to the S-wave (black lines) and the coda-wave (dotted black lines) windows. The mean of the sites was taken as the reference site. The dashed line shows the errors associated with the S-wave solution (1σ ). The grey lines show the solutions of the bootstrap method applied to the S-wave window. categories according to the frequencies of the peaks that are evident in their STFs. Low frequencies: (i) Stations CSM and SCO have a spread peak between 1.5 and 6 Hz. (ii) Stations CAM, GRM and LU4 have a peak in the 3 Hz to 4 Hz band; stations AER, LU1, OAS, OST and SNG and VLR have a narrow peak for a frequency <2Hz. Medium frequencies: (iii) Stations FOR, HC2, PPB and SFT have a spread peak in a frequency band of 5 8 Hz.

9 Site effects at Campi Flegrei 1081 Figure 5. Site amplification functions calculated using the GIT method applied to the vertical components of the S waves (black lines). The mean of the sites was taken as the reference site. The dashed line shows the error associated with the GIT solution (1σ ). 4 DISCUSSION AND CONCLUSIONS Using all of the data sets available, we have studied most of the Campi Flegrei area, and we have correlated the STFs obtained for nearby sites and compared the results with those obtained previously. Station W05 is located on Camaldoli Hill (Fig. 1), just on top of a high-angle scarp that was produced by the collapse of the caldera. Close to this position, the CAM station of the SN was located on a gentle slope (Fig. 1). These sites are very close to each other, and their two STFs calculated with the HVSR technique showed a peak around 3 Hz (W05) and around 4 Hz (CAM) (see Figs 2, 6 and 7). The STF of the CAM station has a peak between 3 and 5 Hz, while the STF of the W05 station has a peak for frequencies <4 Hz. The spectrum of the vertical component of station W05 (see Fig. 5) shows a peak between 4 and 8 Hz, and

10 1082 A. Tramelli et al. Figure 6. Site amplification functions calculated using the HVSR technique applied to the SERAPIS network data set. The analysis was performed on the whole signal (noise and transients). The dashed lines show the error (1σ ). this peak accounts for the small difference that is visible in these two STFs (the peak in the HVSR STF decreases more rapidly and before that of the GIT STF). The differences between stations CAM and W05 are probably due to the difference in the slope of the hill. Nevertheless, there are no particular differences in the geological characteristics of the sites where these two stations were installed, as they are both characterized by a thin sequence of variable cohesive pyroclastics that overlay yellow tuffs (see Fig. 8). Consequently, the peak frequency variation appears to have a weak dependence on the thickness of the sedimentary cover, and this behaviour suggests a topographical effect. These observations agree with the findings of Lee et al. (2009), who documented the STF changes associated with topography roughness in the Taipei basin, in northern Taiwan. Differences in STFs can be seen for the two stations that are located in the Astroni crater floor: stations W03 and ASB2 (Fig. 1). This crater is 2 km wide, and was formed during the recent Campi Flegrei activity (Di Vito et al. 1999). The STF of station W03 calculated with the HVSR method shows a high peak between 3 and 10 Hz (see Fig. 2), but the same peak is not seen in the STF calculated with the GIT for the same station (see Fig. 4). The spectrum of the vertical component of station W03 shows a high de-amplification for the same frequency band (3 10 Hz), where the STF calculated

11 Site effects at Campi Flegrei 1083 Figure 7. Continuing from Fig. 6. with the HVSR has high amplification. Station ASB2 was almost 600 m from station W03, and it shows a STF calculated using the HVSR that is characterized by a low peak for the same frequency band as station W03 (3 10 Hz) (see Fig. 2). The spectrum of the vertical component of station ASB2 shows amplification in a broad band of 2 11 Hz, where the STF calculated with the HVSR has low amplification. The two sites are characterized by broad frequencyband amplification (estimated with the HVSR method), rather than well-defined amplification peaks. The above observations and the knowledge that strong variations can affect the vertical component of the ground motion confirm that the peak in the STF calculated with the HVSR is mainly due to lateral propagation effects (basin shape), as Bindi et al. (2009) noted in their analysis of similar behaviour in the Gubbio basin, central Italy. Their study reported that the sites located inside the basin are characterized by broad frequency-band amplifications, and the amplification pattern does not show strong dependence on the site location, as in the case of the 1-D response. They showed that the HVSR method provided very different amplification patterns for the SSR for sites located inside the basin. They justified this behaviour on the basis that the vertical components are dominated by basin-generated Rayleigh waves, and consequently the HVSR fails to describe the amplification factors. One of the most interesting results was obtained for station TAGG, which was located in the palustrine zone of the Agnano plain. This plain is the result of a volcano-tectonic collapse that occurred during the Agnano-Monte Spina eruption (de Vita et al. 1999). This area is characterized by the presence of thick superficial loose sediments that would result in the high peak that is visible at low frequencies (with both GIT and HVSR). In this case, the stratigraphic effects greatly affect the shape of the STF. The STF of station W10 (Fig. 1) (HVSR technique) shows three amplification peaks at 5, 10 and 20 Hz (see Fig. 2). The GIT STF shows two amplification peaks with slightly lower amplitudes at 10 and 20 Hz (see Fig. 4). This station was located inside the crater of the Capo Miseno tuff cone (see Fig. 8). We noted also that the stations FOR, HC2 and PPB, which were located on the Monte di Procida hill (Fig. 1), were all characterized by STFs that showed a peak between 5 and 8 Hz. These stations were located on tuff in the area of Monte di Procida, close to the caldera border (see Figs 1 and 8). This area is characterized by high structural discontinuities, due to the faults that were active during both the Campanian

12 1084 A. Tramelli et al. Figure 8. Geostructural map of Campi Flegrei modified from Orsi et al. (1996) and Di Vito et al. (1999). The seismic stations used for the analysis are indicated by the blue triangles. ignimbrite and the Neapolitan yellow tuff caldera collapses (Orsi et al. 1996). This area does not have any loose material, similar to the Capo Miseno tuff cone (where station W10 is located). The vertical component spectrum of station W10 shows a de-amplification between 4 and 10 Hz (Fig. 5). This explains the first peak in the HVSR curve that is not present in the GIT. The topographic gradient in this area is high, as it is close to the fault scarp, and this makes the interpretation of the HVSR results more complex. Station SFT was located in the Solfatara crater, and it is characterized by a peak between 4 and 8 Hz. Almost the same results was reported by Petrosino et al. (2008a) using an array located close to this station. In particular, Petrosino (personal communication) found that all of the array station STFs are characterized by a peak around a frequency of 6 Hz. This amplification peak appears to be mainly produced by layering effects, which predominate over topography. Stations AER, OST, LU1 and SNG are all located to the northwest of the city of Naples (Fig. 1), and these are characterized by STFs that show a peak at a frequency of 1.5 Hz. The northern-most station, VLR, shows a smaller peak for the same frequency (amplitude close to 2). The geological structure that characterizes this area is less complex than for the other areas: it is characterized by an almost flat topography and a shallow tuff bedrock (around 30 m deep) (see Fig. 8). Stations AER, OST and VLR lie on variable cohesive pyroclastic material that overlies the Campanian ignimbrite. As station AER is close to the sea, its site is also characterized by the presence of marine sediments. Station LU1 is located almost in the same structure as stations OST and VLR, except for the bedrock, which is of Neapolitan yellow tuff, instead of Campanian ignimbrite. We can conclude here that the site amplifications for the stations located inside the Campanian ignimbrite caldera show more

13 Table 5. The parameters used for the theoretical H/V curves estimations for the sites relative to stations ASB2, W03, W05 and TAGG. Site V S (m s 1 ) ρ (g cm 3 ) h (m) Sequences Watered and reworked deposits ASB Partially lithified pyroclastics Lithified tuff bedrock Watered and reworked deposits W Partially lithified pyroclastics Lithified tuff bedrock W Partially lithified pyroclastics Lithified tuff bedrock Palustrine deposits TAGG Loose pyroclastics Lithified tuff bedrock Notes: The values are extrapolated by Comune di Napoli, Servizio Urbanistica, commissario ad acta L.R. 9/83 (1992), Orsi et al. (1996) and Di Vito et al. (1999). Site effects at Campi Flegrei 1085 Figure 9. Comparison between the theoretical H/V curves (black lines) and the experimental H/V curves (grey lines) for stations ASB2, W03, W05 and TAGG. The values used for the estimates of the theoretical curves are shown in Table 5. complex changes for stations close to each other. This may be due to the high structural and topographical heterogeneity of this area. For the sites where detailed geological stratigraphy is available (ASB2, W03, W05 and TAGG) (Comune di Napoli, Servizio Urbanistica, commissario ad acta L.R. 9/ ; Orsi et al. 1996; Di Vito et al. 1999), we generated the theoretical HVSRs using a programme developed by Herak (2008). This programme computes the theoretical transfer functions of layered soil models using the recursive algorithm proposed by Tsai (1970). The values of the parameters used for the theoretical H/V curve estimates are given in Table 5. As shown in Fig. 9, no match was found between the theoretical and the experimental curves for stations W03 and W05, probably due to basin (W03) or topographical (W05) effects, which can have a major influence with respect to the stratigraphic properties. For station ASB2 a rough match can be found and for station TAGG the theoretical and experimental H/V functions show quite a good match. As mentioned above, station TAGG is located in a palustrine and a topographically flat zone, and the stratigraphic effects predominate. The inner zone of the Campi Flegrei caldera behaves as a basin, that affects the propagation mainly for the vertical component. As a consequence, the application of the HVSR technique in this area is inappropriate. Stations TAGG and SFT are characterized by waterdominated, low-density, shallow layers, which predominately affect the STFs. ACKNOWLEDGMENTS The authors wish to thank the mobile seismic network team, and in particular Mario La Rocca, who provided the data. We are also grateful to Aldo Zollo and Maurizio Vassallo for making the SER- APIS data set available. We also wish to thank Simona Petrosino, Paola Cusano and Luca D Auria for their useful suggestions. Particular thanks go to Marijan Herak, who provided us with useful suggestions for the use of the HVSR Matlab programme. The authors wish to thank Stephane Drouet and an anonymous reviewer for their useful suggestions. This work was mainly supported by the Civil Defence of Italy (DPC) and the Istituto Nazionale di Geofisica e Vulcanologia (INGV) in the framework of the project entitled

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