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HVSR deep mapping tested down to ∼ 1.8 km in Po Plane Valley, Italy
Physics of the Earth and Planetary Interiors xxx (2016) xxx–xxx
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HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy Francesco Mulargia ⇑, Silvia Castellaro Dipartimento di Fisica e Astronomia, Settore di Geofisica, Università di Bologna, viale Berti Pichat 8, 40127 Bologna, Italy
a r t i c l e
i n f o
Article history: Received 15 July 2015 Received in revised form 26 July 2016 Accepted 12 August 2016 Available online xxxx Keywords: Seismic noise Passive imaging Subsoil exploration
a b s t r a c t The Horizontal to Vertical Spectral Ratio – HVSR – of seismic noise is extensively used in seismic microzonation for its capability to provide a good approximation to the subsoil main resonance frequencies of geotechnical and seismic engineering interest. This implies, in turn, that it has also an approximate passive subsoil mapping capability independent of the level of noise illumination, albeit limited to relatively shallow depths, since tilt sensitivity makes HVSR unreliable below 0.1 Hz. However, we have experimentally verified that HVSR subsoil mapping capability extends to depths in the kilometer range by applying it to the largest sedimentary basin of Italy, the Po Plain Valley. There, we were able to resolve the major stratigraphic discontinuities down to the sediment-bedrock interface, for which we estimated a depth of 1.6 km with a 25% uncertainty, while the surface mapped from oil exploration indicates a depth of 1.8 km. Quarter of an hour recordings gave always stable signals that, fitted to synthetic curves using as a constraint the parameters of the shallow subsoil, provided a stratigraphic map consistent with the independent survey. This candidates HVSR as a fast and inexpensive, first-order subsoil mapping tool down to depths of geological and exploration interest. Ó 2016 Elsevier B.V. All rights reserved.
1. Introduction Passive seismic imaging of subsoil has only rarely been performed using the Horizontal to Vertical Spectral Ratio (HVSR), which rather gained popularity as a semi-empirical tool for seismic microzonation (Nogoshi and Igarashi, 1971; Nakamura, 1976, 1989; Lermo and Chavez-Garcia, 1994; Mucciarelli and Gallipoli, 2001; D’Amico et al., 2008; Motazedian et al., 2011; Del Monaco et al., 2013). There have been repeated attempts to put HVSR on firm theoretical grounds, but a general formulation of HVSR does not exist. The classic interpretation relies on the assumption that the spectral ratio is only affected by vertical standing body waves. This approach, taking the subsoil as a layered half-space with stiffness and rigidity (mostly) monotonically increasing with depth, and treating it with the 1-D Haskell–Thomson approach, prescribes that (cf. Herak, 2008) the only standing waves are those vertically trapped in the layers, so that the vertical particle motion is due to P waves while the horizontal motion is due to S waves. Therefore, the peaks in HVSR correspond to the relative peaks of S and troughs of P waves, the vice versa occurring for HVSR troughs. An alternative approach assumes that seismic noise, still treated with the 1-D Haskell–Thomson formulation, is instead solely composed of surface waves and that HVSR is tied to the ellipticity of ⇑ Corresponding author. E-mail address: [email protected] (F. Mulargia).
Rayleigh waves, with a possible contribution also from Love waves (e.g., Fäh et al., 2001; Malischewsky and Scherbaum, 2004, Arai and Tokimatsu, 2004; Bonnefoy-Claudet et al., 2008; Van der Baan, 2009). From the experimental side, the first application of HVSR to subsoil imaging was that of Ibs-Von Seht and Wohlenberg (1999). The object of that study was to map the depth of the Rhine sedimentary basin to the bedrock and relied on assuming an exponential shear wave velocity V s ðzÞ increase with depth in the sediment due to gravitational compaction. The study was effected by using the S-wave transfer function and considering only the fundamental mode and a single layer. Later, by conversely assuming a Rayleigh wave composition of seismic noise, the HVSR approach was applied to single-station passive stratigraphic imaging through modal summation (Fäh et al., 2001). Also in this case the study was limited to shallow depth (< 50 m), but allowed for multiple layers. The V s ðzÞ velocity profile was obtained through a synthetic fit, achieving a good internal consistency, although the uniqueness of the solution – as in any inverse nonlinear problem – was obviously not guaranteed. Other attempts to use HVSR for shallow subsoil imaging were made (e.g., Parolai et al., 2002; D’Amico et al., 2008; Gosar, 2007; Herak, 2008; Gosar and Lenart, 2010; Castellaro and Mulargia, 2009), which can all be considered variants of the above. The composition of seismic noise is still a wide open problem, possibly not admitting a solution. An extensive beamforming anal-
http://dx.doi.org/10.1016/j.pepi.2016.08.002 0031-9201/Ó 2016 Elsevier B.V. All rights reserved.
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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ysis of array experimental data (Ruigrok et al., 2011) showed a complex picture: at the site these authors considered, seismic noise was found composed essentially of surface waves at frequencies below 0.09 Hz, while at frequencies between 0.09 Hz and 1 Hz the ray parameters showed a predominance of P body waves in the vertical component, and a difficult to sort combination of body and surface waves on the horizontal one, with the relative balance depending on specific frequency and instantaneous noise sources and raypaths. At higher frequencies, the balance is likely to be even more intricate. Summing up, since the composition of noise is not amenable to a definite recipe, an accurate HVSR theoretical treatment appears unrealistic. A more tenable approach is to ‘‘rely on convergence”. In fact, Tuan et al. (2011) showed that, at least for a single layer over a half space, convergence within a few percent occurs over a large interval of parameters between the HVSR peak originated by Rayleigh surface waves and by body waves. Therefore, it seems appropriate to treat HVSR as simply composed of either body or surface waves, while keeping well in mind the inevitably approximate nature of the results. The question we address in the present work is one of mere phenomenological type: HVSR has been shown to provide a passive mapping capability in the subsoil of engineering, geotechnical and shallow geological interest, i.e. down to depths of the order of 100 m. Does it have a similar capability at the substantially larger depths of interest for geologic stratigraphy and subsoil resource exploration? And, if so, is it a practically usable – and useful – tool? To ascertain this, we performed a series of HVRS measurements in the largest sedimentary basin of Italy, the Po Plain Valley, which comprehends most of North and North-central Italy, extending from Turin to Milan, Venice and Bologna. In particular, our analysis regarded two specific areas of North-Eastern Italy around Venice, both located in the proximity of the Adriatic Sea (see Fig. 1). We chose them for the presence of a pre-Plio-Pleistocene bedrock at
depths varying from about 0.4 km to 3 km, according to a morphology which was independently mapped during the past decades in the course of extensive hydrocarbon exploration (AGIP, 1988). The first group of our HVSR measurements was located on a transect above a bedrock sloping from a depth of 0.5 km to 1.1 km on a baseline of approximately 30 km. 2. HVSR subsoil mapping If no spatially defined predominant source of microtremors exists near the measurement point, like a large nearby industrial plant or a freeway, seismic noise should in principle consist of a diffuse-like wavefield, i.e., by an isotropic superposition of plane waves with apparently random phase (e.g., Lachet and Bard, 1994). However, isotropy is experimentally found to be hardly ever realized in practice and, using appropriate techniques like polarization, triangulation or array techniques (see e.g., Friedrich et al., 1998; Ohori et al., 2002; Mulargia, 2012), it is possible to sort the single constituent wavetrains of seismic noise, which appear to vary with time at the scale of seconds. Numerical simulations (Bonnefoy-Claudet et al., 2006) confirm that HVSR is tied to the local spectral response to either Rayleigh waves or to a variable mixture – depending on the distance from the sources – of body P, S and surface Rayleigh and Love waves, with a generally anisotropic spatial distribution of wave vectors. In short, representing noise as a time independent superposition of stochastic sources with an isotropic distribution of random wavetrains, i.e., by a diffuse wavefield, is unrealistic. However, this does not prevent HVSR a passive subsoil imaging capability: one well known phenomenological feature is that, after removing the transients produced by disturbances in the near proximity of the instrument, the normalization and deconvolution operated by HVSR produce spectral patterns which are remarkably stable in time, and this stability is at the basis of the HVSR success
Fig. 1. The map of the bedrock (AGIP, 1988), with depth indicated by the contour lines in meters. The red dots indicate the location in the Po Plain (Italy) of the single station measurement points in this study: the 7 points of the transect A-B and Chioggia. The blue dot indicates the point of the array measurements of Mulargia and Castellaro (2008). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
F. Mulargia, S. Castellaro / Physics of the Earth and Planetary Interiors xxx (2016) xxx–xxx
in seismic microzonation. In fact, the HVSR standard deviations become a fraction of unit over intervals of 103 s (e.g., Castellaro and Mulargia, 2009), setting the optimum HVSR survey time at about 15–20 min. Some modest differences, of clear meteorological origin, are apparent between seasons; similar differences, of clear anthropogenic origin, occur between working and repose hours (for some examples, see e.g., Castellaro, in press). These are most likely due to different noise source spectra, spatial location and wave components which HVSR is unable to fully equalize. Nevertheless, we can phenomenologically conclude that HVSR has always a predominant dependence on local subsoil structure rather than on source, as it could be also expected from its well known and widely used capability to provide an approximate measure of the local subsoil dominant response frequencies. Then, by definition, if an independent constraint is available on seismic wave velocity or on stratigraphy (usually either one or both available at shallow depth), the resonance frequency can be translated into depth and used for passive subsoil mapping. 2.1. HVSR subsoil mapping: the HVSR frequency limits As a microzonation tool, HVSR analysis is applied to the frequencies of engineering interest, i.e., from a fraction of a Hz to 10 Hz. Since an open-wave guide resonant frequency f translates into depth z at quarter-wavelength, i.e., z ¼ v =4f , larger depths are related to lower frequencies. Unfortunately, horizontal noise components are very sensitive to ground tilt, since gravity induces comparatively large horizontal cross-amplitudes even at small tilt angles (see e.g., Rodgers, 1968). The latter are amplified by the functional form of HVSR, which becomes highly unstable at frequencies K 0:1 Hz, where most meteoric tilt occurs. This is clearly apparent in Fig. 2, which reproduces Fig. 10 of Castellaro and Mulargia (2012), relative to one year continuous recording of a broadband Streckeisen STS-2 station of the MedNet network. The existence of such a lower frequency operational limit f lim for HVSR is the one that, in turn, sets the HVSR depth limit zlim . By assuming the predominance of the surface open-waveguide fundamental mode, which generally carries the largest fraction of wave energy (e.g., Ben-Menahem and Singh, 1981), the depth limit is
zlim ’
hVsi hVsi 2:5hVsi 4f lim 4 0:1
ð1Þ
Therefore, since the average shear wave velocityhVsi in the shallow sediments is of the order of 500–1000 m/s, the zlim theoretically
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resolvable by HVSR is restricted to the first very few kilometers, a ceiling explored by the present experimental study. It is important to emphasize also that the existence of a HVSR lower frequency limit at 0.1 Hz, empirically noted also by Nishitsuji et al. (2014), makes broadband seismographs useless in HVSR. In fact, the key features of an ideal HVSR instrument appear to reside not in bandwidth but in electronic stability and resolution: using 24 bit good quality electronic circuitry, sensors with a few Hz eigenfrequency have proven entirely adequate to measure HVSR over the whole frequency range down to the 0.1 Hz limit (Strollo et al., 2008; Castellaro and Mulargia, 2009; Chatelain and Guillier, 2013). 2.2. HVSR subsoil mapping: measurements and pre-analysis We performed all tremor measurements using Tromino tromographs, which are triaxial, 3 velocimeter + 3 accelerometer, 24 bit, 0.1–256 Hz portable instruments expressly designed to digitally record seismic noise rather than earthquake signals. Our procedure of spectral analysis and model fitting uses the Grilla code. Namely, spectral analysis treats the waveforms according to Gabór approach, i.e., these are sectioned in contiguous time segments of fixed length (we used 20 s throughout) and FFT transformed under appropriate central windowing (we used triangular kernels with a 10% smoothing around the central frequency); the average spectra relative to the whole duration of each measurement are then computed for the vertical and horizontal components, the two horizontal ones are square-root diagonally summed and the HVSR computed. Each seismic noise acquisition lasted 15–20 min and was repeated at different times of the day and at different days of the week, finding large differences in absolute noise amplitude on the single components, but a good stability of the HVSR curve (Fig. 3). A similar stability was observed (Fig. 4) under the seasonal variation of meteorological conditions, which are the known main factor affecting seismic noise amplitude (e.g., Hasselmann, 1963; Friedrich et al., 1998); these were studied through two series of continuous HVSR measurements completed in August 2015, respectively centered in spring and autumn, when the largest meteorological changes occur. Such measurements were performed at the supposedly most critical site – Chioggia – which is located above the deepest bedrock interface of the Po Valley – and at a site in Venice mainland, where the bedrock interface is slightly shallower (blue dot in Fig. 1). A modest dependence of the HVSR curve on time of the day and season was found, with
Fig. 2. The HVSR signal relative to the whole signal recorded during the whole year 2008 by the Streckeisen STS-2 broadband seismograph of the MedNet SLNM-TRI station (from Fig. 10 of Castellaro and Mulargia, 2012).
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 3. The vertical (top panel) and horizontal component (mid panel) of noise spectral amplitude measured in good and foul weather, showing large variations between the two conditions. Conversely, the HVSR for the same data (bottom panel) remains remarkably stable, providing essentially the same picture. In this panel thick lines represent the average values, thin lines the 2r confidence bands (cf. Fig. 1 of Castellaro, in press).
the HVSR shape always clearly apparent down to the lower frequency HVSR peak, which is the marker of the deep sedimentbedrock interface (Fig. 4). 2.3. HVSR subsoil mapping: the procedure If one assumes that no strong lateral discontinuity is present near the observation point, a local 1-D approximation can be used. This allows to largely simplify the calculations reducing them to evaluate the eigenfunctions of the Rayleigh equation (see e.g., Haskell, 1953; Knopoff, 1964), and is also consistent with the first-order approximate nature of the present approach. The problem is thus reduced to 1-D modal summation, which provides directly the wavefield in the spectral form required to evaluate HVSR. Note how this, in a 2-D (or 3-D) treatment, coincides with a body wave assumption of noise composition. Obviously, due to the interference of P and SV waves, at some distance from the source this evolves in a surface Rayleigh wave approach. The Knop-
off–Schwab expansion (Schwab et al., 1984) was shown to be free of numerical instabilities up to frequencies well beyond those involved by the size of the scatterers in the Earth’s crust (e.g., Ben-Menahem and Singh, 1981). Several specific formulations are available for calculating the 1-D coupling coefficients (e.g., BenMenahem and Singh, 1981). To effectively treat all numerical stability problems, we used the latter with Dunkin (1965) stability correction, extending summation up to the first five Rayleigh modes, since these were found to account for virtually all energy transmission (cf. Mulargia and Castellaro, 2013). A variety of behaviors has been suggested for the anelastic wave scattering parameter Q (Ben-Menahem and Singh, 1981; Seale and Archuleta, 1989; Fukushima et al., 1992; Olsen et al., 2003; : Assimaki et al., 2005; Satoh, 2006), with values Q ’ 3¼15 for the : soil and Q ’ 50¼120 for the superficial bedrock, with a mild frequency dependence. According to our passive array measurement technique of both elastic and anelastic parameters (Mulargia and Castellaro, 2013), Q was measured in the Southern part of the same
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 4. The results of a series of HVSR measurements completed in August 2015: panel A shows the Dec–Mar monthly averaged HVSR at Chioggia, while panel B shows the same in May–Aug at the site where also the array measurements were taken (blue dot in Fig. 1). In both pictures the thick line is the HVSR curve and the thin ones are the 2r confidence bands.
: sedimentary basin, obtaining Q ’ 10¼12 at shallow depth, and an approximate independence of frequency (ibid.). In the synthetic fit, we therefore simply took Q ¼ 10 for soil, and Q ¼ 100 for the bedrock, independent of frequency (cf. also Malagnini, 1996). The synthetic HVSR curve is linked to impedance contrasts between successive strata and provides per se a subsoil image in terms of frequency. Translating this into a stratigraphic image in depth requires, as we already noted, an independent calibration. A simple and practically effective approach (Castellaro and Mulargia, 2009) is to use as a constraint one of the shallow depth interfaces which is independently known from direct geotechnical surveys (drilling, penetration tests, outcrops, trenches, construction works, etc.) or the near surface Vp and Vs profiles inferred from passive or active array surveys. It is important to note that our fit considers both the frequency and the amplitude of the HVSR peaks, with the latter depending primarily on the S and P wave impedance contrasts, but being also inversely proportional to the spectral smoothing function. The latter is subjectively chosen as a compromise between resolution and stability (cf. e.g., Konno and Ohmachi, 1998) and therefore the peak height does not represent an absolute quantity, but the relative amplitude of the contrasts.
3. Results and discussion The results of the analysis of the recordings on the transect is shown in Fig. 5. In all cases the measurements provided clear markers for the bedrock interface, which was always apparent at frequencies below 0.5 Hz. Comparing the map produced by the HVSR fit with the one independently produced by well data, a good match was obtained along the 7 station transect (Fig. 5). Namely, using as a constraint the near surface Vs profile derived by an independent passive array study (Mulargia and Castellaro, 2008), the
HVSR fit was capable to resolve correctly both a major gravelly bank at 0.2 km depth and the non-horizontal dolomitic bedrock sloping from 0.5 km depth in the N-E part to more than 1.1 km in the SW part, with a dip angle larger than 1 . This suggests that even when the wavelengths involved are comparable with the transect length (about 30 km), modest bedrock slopes can be properly resolved by the 1-D local modes (Ben-Hador and Buchen, 1999). Similarly clear images were obtained at Chioggia (Fig. 6), where the HVSR constrained fit gave a depth estimate of about 1.6 km, and where the surface interpolated from exploration data (Fig. 1) place the bedrock at a depth of 1.8 km. The marker of this deep interface is a HVSR peak at about 0.25 Hz, which was always clearly apparent, with a marginal influence of the daily and seasonal variations in noise amplitude (see Fig. 4). Summing up, quarter of an hour seismic noise HVSR recordings allowed to consistently resolve the major stratigraphic subsoil discontinuities down to the 2 km (see Figs. 6 and 1) depths reached by the sediment Pre-Plio-Pleistocene bedrock interface at the basis of the Po Plane Valley. While this may sound unimpressive, given that passive multi-station seismic imaging has been successfully applied down to the earth’s mantle (e.g., Pavlis, 2003) and core (e.g., Lin et al., 2013), such results are in a completely different category than HVSR. In fact, their imaging capability depends crucially on the level of passive illumination, i.e., since the origin of seismic noise is essentially the nonlinear interaction among ocean waves and among them and the ocean bottom, it is tied to sea storms, and is therefore largely variable. Such passive imaging techniques require to ‘‘capture the signal” with large arrays and long observation times, waiting months for huge storms to provide a sufficient illumination. HVSR noise subsoil mapping is also a passive imaging technique, but a very different one: first of all, it operates at higher frequencies, since below 0:1 Hz, due to ground tilt, HVSR becomes unreliable. This limits the HVSR subsoil mapping range to the first very few kilometers, but the normalization of the hori-
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 5. The V s subsoil image inferred from the HVSR synthetic fit along the transect A-B (see Fig. 1). The black line illustrates the bedrock depth as per the AGIP (1988) map. The apparent bifurcation of the 1.2 km/s region below 0.9 km depth around the middle of the transect is an artefact of the contour mapping.
Fig. 6. Imaging the V s profile vs depth from the HVSR synthetic fit of a 20’ HVSR recording at Chioggia (see Fig. 1). The bedrock depth is estimated at 1.6 km.
zontal spectrum by the vertical one leads to analyze a signal which has little in common with the single spectrum of the vertical motion, generally used in deep seismic imaging. As we already said, the normalization makes the HVSR nearly independent of absolute noise level and cancels out the effect of sources, sorting the effect of local subsoil structure. Beyond depth, the other major drawback of HVSR is its intrinsically approximate nature, which limits velocity and depth estimates to inaccuracies of 25%. However, these may be not much worse than those inherent to the spatial interpolation of exploration data, like it possibly happens also in the present case. 4. Conclusions The goal of the present study was to ascertain the deep stratigraphic mapping capability of HVSR, a tool so far ubiquitous in seismic microzonation for its inexpensive and reliable capability
to measure the elastic response of shallow subsoil. Since the latter response, when combined with an independent constraint as the shallow depth velocity profile or any known interface, can be translated into a stratigraphic image, HVSR is also a subsoil imaging tool, already used with success at shallow depths. We have experimentally verified that such a mapping capability extends to the few kilometer depths of geological and geophysical exploration interest. Namely, we have found that, with 15–20 min single-station measurements, HVSR is capable to resolve the major stratigraphic interfaces of the Po Plane Valley, Italy, down to depths of the order of 2 km. HVSR allowed to effectively resolve impedance contrasts around 50%, and therefore the synthetic fit is in principle able to identify not only sediment-bedrock interfaces but also soil-soil and rock-rock ones. Finally, we found that mild deviations from planarity can be readily resolved. Summing up, the positive results obtained stand in favor of HVSR as a tool for fast, low-cost, first-order subsoil mapping down
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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to the kilometer range. Only interfaces with a larger than 50% impedance contrast can be resolved, and the uncertainties in the depth estimates are tied to the 25% uncertainties in the velocity estimates and to the constraint – interface depth difference. If the constraint is superficial, the depth uncertainties are also about 25%, but if the constraint has a depth comparable with the interface, measured in the same region by a well or by other independent survey, they can be substantially smaller. In our case, by using only near surface constraints, the deep Po Plane Valley interface at Chioggia is estimated at 1.6 km, while independent exploration data set it at 1:8 km. HVSR subsoil depth mapping may regard a variety of practical applications, including geologic modeling, large scale preliminary surveying for resources, areal extension of surveys performed with standard methods (like, for example, mapping the interface topography around a well), independent cross-checking of previous surveys, etc. The technique appears to have a strong practical potential especially at the larger depths it can reach: while survey costs with standard techniques increase at least linearly with depth, HVSR, requiring neither to drill boreholes nor to deploy long arrays for seismic exploration – and not even to use longer observation times – remains identically inexpensive.
Acknowledgements This work was completed with a RFO contribution from the Università di Bologna.
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Development of a Vs30 (NEHRP) map for the city of Ottawa, Ontario, Canada. Can. Geotech. J. 48, 458– 472. http://dx.doi.org/10.1139/T10-081. Mucciarelli, M., Gallipoli, M.R., 2001. A critical review of 10 years of microtremor HVSR technique. Boll. Geof. Teor. Appl. 42, 255–266. Mulargia, F., 2012. The seismic noise wavefield is not diffuse. J. Acoust. Soc. Am. 131, 2853–2858. http://dx.doi.org/10.1121/1.3689551. Mulargia, F., Castellaro, S., 2008. Passive imaging in nondiffuse acoustic wavefields. Phys. Rev. Lett. 100, 2185014. http://dx.doi.org/10.1103/ PhysRevLett.100.218501. Mulargia, F., Castellaro, S., 2013. A seismic passive imaging step beyond SPAC and ReMi. Geophysics 78, KS63–KS72. Nakamura, Y., 1976. Seismic energy transmission in the lunar surface zone determined from signals generated by movement of lunar rovers. Bull. Seismol. Soc. Am. 66, 593–606. Nakamura, Y., 1989. A method for dynamic characteristics estimates of sub-surface using microtremor on the round surface. Quart. Rep. Railway Tech. Res. Inst. 30, 25–33. Nishitsuji, Y., Ruigrok, E., Gomez, M., Draganov, D., 2014. Global-phase H/V spectral ratio for delineating the basin in the Malarge region, Argentina. Seismol. Res. Lett. 85, 1004–1011. http://dx.doi.org/10.1785/0220140054. Nogoshi, M., Igarashi, T., 1971. On the amplitude characteristics of mi-crotremor (part 2). J. Seism. Soc. Japan 24, 26–40, In Japanese with English abstract. Ohori, M., Nobata, A., Wakamatsu, K.A., 2002. A comparison of ESAC and FK methods of estimating phasevelocity using arbitrarily shaped microtremor arrays. Bull. Seismol. Soc. Am. 92, 2323–2332. Olsen, K.B., Day, S.M., Bradley, C.R., 2003. Estimation of Q for long-Period (2 sec) Waves in the Los Angeles basin. Bull. Seismol. Soc. Am. 93, 627–638. Parolai, S., Bormann, P., Milkereit, C., 2002. New relationship between Vs, thickness of sediments, and resonance frequency calculated by the H/V ratio of seismic noise for the Cologne area (Germany). Bull. Seismol. Soc. Am. 92, 2521–2527. Pavlis, G.L., 2003. Imaging the earth with passive seismic arrays. The Leading Edge 22, 224–231. Rodgers, P.W., 1968. The response of the horizontal pendulum seismometer to Rayleigh and Love waves, tilt, and free oscillations of the earth. Bull. Seismol. Soc. Am. 58, 1384–1406. Ruigrok, E., Campman, X., Wapenaar, K., 2011. Extraction of P-wave reflections from microseisms. Comptes Rendus Geosci. 343. Satoh, T., 2006. Inversion of QS of deep sediments from surface-to-borehole spectral ratios considering obliquely incident SH and SV waves. Bull. Seismol. Soc. Am. 96, 943–956. Schwab, F., Nakanishi, K., Cuscito, M., Panza, G.F., Liang, G., Frez, J., 1984. Surface waves computations and the synthesis of theoretical seismograms at high frequencies. Bull. Seismol. Soc. Am. 74, 1555–1578. Seale, S., Archuleta, R.J., 1989. Site amplification and attenuation of strong ground motion. Bull. Seismol. Soc. Am. 79, 1673–1696. 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Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy Francesco Mulargia ⇑, Silvia Castellaro Dipartimento di Fisica e Astronomia, Settore di Geofisica, Università di Bologna, viale Berti Pichat 8, 40127 Bologna, Italy
a r t i c l e
i n f o
Article history: Received 15 July 2015 Received in revised form 26 July 2016 Accepted 12 August 2016 Available online xxxx Keywords: Seismic noise Passive imaging Subsoil exploration
a b s t r a c t The Horizontal to Vertical Spectral Ratio – HVSR – of seismic noise is extensively used in seismic microzonation for its capability to provide a good approximation to the subsoil main resonance frequencies of geotechnical and seismic engineering interest. This implies, in turn, that it has also an approximate passive subsoil mapping capability independent of the level of noise illumination, albeit limited to relatively shallow depths, since tilt sensitivity makes HVSR unreliable below 0.1 Hz. However, we have experimentally verified that HVSR subsoil mapping capability extends to depths in the kilometer range by applying it to the largest sedimentary basin of Italy, the Po Plain Valley. There, we were able to resolve the major stratigraphic discontinuities down to the sediment-bedrock interface, for which we estimated a depth of 1.6 km with a 25% uncertainty, while the surface mapped from oil exploration indicates a depth of 1.8 km. Quarter of an hour recordings gave always stable signals that, fitted to synthetic curves using as a constraint the parameters of the shallow subsoil, provided a stratigraphic map consistent with the independent survey. This candidates HVSR as a fast and inexpensive, first-order subsoil mapping tool down to depths of geological and exploration interest. Ó 2016 Elsevier B.V. All rights reserved.
1. Introduction Passive seismic imaging of subsoil has only rarely been performed using the Horizontal to Vertical Spectral Ratio (HVSR), which rather gained popularity as a semi-empirical tool for seismic microzonation (Nogoshi and Igarashi, 1971; Nakamura, 1976, 1989; Lermo and Chavez-Garcia, 1994; Mucciarelli and Gallipoli, 2001; D’Amico et al., 2008; Motazedian et al., 2011; Del Monaco et al., 2013). There have been repeated attempts to put HVSR on firm theoretical grounds, but a general formulation of HVSR does not exist. The classic interpretation relies on the assumption that the spectral ratio is only affected by vertical standing body waves. This approach, taking the subsoil as a layered half-space with stiffness and rigidity (mostly) monotonically increasing with depth, and treating it with the 1-D Haskell–Thomson approach, prescribes that (cf. Herak, 2008) the only standing waves are those vertically trapped in the layers, so that the vertical particle motion is due to P waves while the horizontal motion is due to S waves. Therefore, the peaks in HVSR correspond to the relative peaks of S and troughs of P waves, the vice versa occurring for HVSR troughs. An alternative approach assumes that seismic noise, still treated with the 1-D Haskell–Thomson formulation, is instead solely composed of surface waves and that HVSR is tied to the ellipticity of ⇑ Corresponding author. E-mail address: [email protected] (F. Mulargia).
Rayleigh waves, with a possible contribution also from Love waves (e.g., Fäh et al., 2001; Malischewsky and Scherbaum, 2004, Arai and Tokimatsu, 2004; Bonnefoy-Claudet et al., 2008; Van der Baan, 2009). From the experimental side, the first application of HVSR to subsoil imaging was that of Ibs-Von Seht and Wohlenberg (1999). The object of that study was to map the depth of the Rhine sedimentary basin to the bedrock and relied on assuming an exponential shear wave velocity V s ðzÞ increase with depth in the sediment due to gravitational compaction. The study was effected by using the S-wave transfer function and considering only the fundamental mode and a single layer. Later, by conversely assuming a Rayleigh wave composition of seismic noise, the HVSR approach was applied to single-station passive stratigraphic imaging through modal summation (Fäh et al., 2001). Also in this case the study was limited to shallow depth (< 50 m), but allowed for multiple layers. The V s ðzÞ velocity profile was obtained through a synthetic fit, achieving a good internal consistency, although the uniqueness of the solution – as in any inverse nonlinear problem – was obviously not guaranteed. Other attempts to use HVSR for shallow subsoil imaging were made (e.g., Parolai et al., 2002; D’Amico et al., 2008; Gosar, 2007; Herak, 2008; Gosar and Lenart, 2010; Castellaro and Mulargia, 2009), which can all be considered variants of the above. The composition of seismic noise is still a wide open problem, possibly not admitting a solution. An extensive beamforming anal-
http://dx.doi.org/10.1016/j.pepi.2016.08.002 0031-9201/Ó 2016 Elsevier B.V. All rights reserved.
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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ysis of array experimental data (Ruigrok et al., 2011) showed a complex picture: at the site these authors considered, seismic noise was found composed essentially of surface waves at frequencies below 0.09 Hz, while at frequencies between 0.09 Hz and 1 Hz the ray parameters showed a predominance of P body waves in the vertical component, and a difficult to sort combination of body and surface waves on the horizontal one, with the relative balance depending on specific frequency and instantaneous noise sources and raypaths. At higher frequencies, the balance is likely to be even more intricate. Summing up, since the composition of noise is not amenable to a definite recipe, an accurate HVSR theoretical treatment appears unrealistic. A more tenable approach is to ‘‘rely on convergence”. In fact, Tuan et al. (2011) showed that, at least for a single layer over a half space, convergence within a few percent occurs over a large interval of parameters between the HVSR peak originated by Rayleigh surface waves and by body waves. Therefore, it seems appropriate to treat HVSR as simply composed of either body or surface waves, while keeping well in mind the inevitably approximate nature of the results. The question we address in the present work is one of mere phenomenological type: HVSR has been shown to provide a passive mapping capability in the subsoil of engineering, geotechnical and shallow geological interest, i.e. down to depths of the order of 100 m. Does it have a similar capability at the substantially larger depths of interest for geologic stratigraphy and subsoil resource exploration? And, if so, is it a practically usable – and useful – tool? To ascertain this, we performed a series of HVRS measurements in the largest sedimentary basin of Italy, the Po Plain Valley, which comprehends most of North and North-central Italy, extending from Turin to Milan, Venice and Bologna. In particular, our analysis regarded two specific areas of North-Eastern Italy around Venice, both located in the proximity of the Adriatic Sea (see Fig. 1). We chose them for the presence of a pre-Plio-Pleistocene bedrock at
depths varying from about 0.4 km to 3 km, according to a morphology which was independently mapped during the past decades in the course of extensive hydrocarbon exploration (AGIP, 1988). The first group of our HVSR measurements was located on a transect above a bedrock sloping from a depth of 0.5 km to 1.1 km on a baseline of approximately 30 km. 2. HVSR subsoil mapping If no spatially defined predominant source of microtremors exists near the measurement point, like a large nearby industrial plant or a freeway, seismic noise should in principle consist of a diffuse-like wavefield, i.e., by an isotropic superposition of plane waves with apparently random phase (e.g., Lachet and Bard, 1994). However, isotropy is experimentally found to be hardly ever realized in practice and, using appropriate techniques like polarization, triangulation or array techniques (see e.g., Friedrich et al., 1998; Ohori et al., 2002; Mulargia, 2012), it is possible to sort the single constituent wavetrains of seismic noise, which appear to vary with time at the scale of seconds. Numerical simulations (Bonnefoy-Claudet et al., 2006) confirm that HVSR is tied to the local spectral response to either Rayleigh waves or to a variable mixture – depending on the distance from the sources – of body P, S and surface Rayleigh and Love waves, with a generally anisotropic spatial distribution of wave vectors. In short, representing noise as a time independent superposition of stochastic sources with an isotropic distribution of random wavetrains, i.e., by a diffuse wavefield, is unrealistic. However, this does not prevent HVSR a passive subsoil imaging capability: one well known phenomenological feature is that, after removing the transients produced by disturbances in the near proximity of the instrument, the normalization and deconvolution operated by HVSR produce spectral patterns which are remarkably stable in time, and this stability is at the basis of the HVSR success
Fig. 1. The map of the bedrock (AGIP, 1988), with depth indicated by the contour lines in meters. The red dots indicate the location in the Po Plain (Italy) of the single station measurement points in this study: the 7 points of the transect A-B and Chioggia. The blue dot indicates the point of the array measurements of Mulargia and Castellaro (2008). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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in seismic microzonation. In fact, the HVSR standard deviations become a fraction of unit over intervals of 103 s (e.g., Castellaro and Mulargia, 2009), setting the optimum HVSR survey time at about 15–20 min. Some modest differences, of clear meteorological origin, are apparent between seasons; similar differences, of clear anthropogenic origin, occur between working and repose hours (for some examples, see e.g., Castellaro, in press). These are most likely due to different noise source spectra, spatial location and wave components which HVSR is unable to fully equalize. Nevertheless, we can phenomenologically conclude that HVSR has always a predominant dependence on local subsoil structure rather than on source, as it could be also expected from its well known and widely used capability to provide an approximate measure of the local subsoil dominant response frequencies. Then, by definition, if an independent constraint is available on seismic wave velocity or on stratigraphy (usually either one or both available at shallow depth), the resonance frequency can be translated into depth and used for passive subsoil mapping. 2.1. HVSR subsoil mapping: the HVSR frequency limits As a microzonation tool, HVSR analysis is applied to the frequencies of engineering interest, i.e., from a fraction of a Hz to 10 Hz. Since an open-wave guide resonant frequency f translates into depth z at quarter-wavelength, i.e., z ¼ v =4f , larger depths are related to lower frequencies. Unfortunately, horizontal noise components are very sensitive to ground tilt, since gravity induces comparatively large horizontal cross-amplitudes even at small tilt angles (see e.g., Rodgers, 1968). The latter are amplified by the functional form of HVSR, which becomes highly unstable at frequencies K 0:1 Hz, where most meteoric tilt occurs. This is clearly apparent in Fig. 2, which reproduces Fig. 10 of Castellaro and Mulargia (2012), relative to one year continuous recording of a broadband Streckeisen STS-2 station of the MedNet network. The existence of such a lower frequency operational limit f lim for HVSR is the one that, in turn, sets the HVSR depth limit zlim . By assuming the predominance of the surface open-waveguide fundamental mode, which generally carries the largest fraction of wave energy (e.g., Ben-Menahem and Singh, 1981), the depth limit is
zlim ’
hVsi hVsi 2:5hVsi 4f lim 4 0:1
ð1Þ
Therefore, since the average shear wave velocityhVsi in the shallow sediments is of the order of 500–1000 m/s, the zlim theoretically
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resolvable by HVSR is restricted to the first very few kilometers, a ceiling explored by the present experimental study. It is important to emphasize also that the existence of a HVSR lower frequency limit at 0.1 Hz, empirically noted also by Nishitsuji et al. (2014), makes broadband seismographs useless in HVSR. In fact, the key features of an ideal HVSR instrument appear to reside not in bandwidth but in electronic stability and resolution: using 24 bit good quality electronic circuitry, sensors with a few Hz eigenfrequency have proven entirely adequate to measure HVSR over the whole frequency range down to the 0.1 Hz limit (Strollo et al., 2008; Castellaro and Mulargia, 2009; Chatelain and Guillier, 2013). 2.2. HVSR subsoil mapping: measurements and pre-analysis We performed all tremor measurements using Tromino tromographs, which are triaxial, 3 velocimeter + 3 accelerometer, 24 bit, 0.1–256 Hz portable instruments expressly designed to digitally record seismic noise rather than earthquake signals. Our procedure of spectral analysis and model fitting uses the Grilla code. Namely, spectral analysis treats the waveforms according to Gabór approach, i.e., these are sectioned in contiguous time segments of fixed length (we used 20 s throughout) and FFT transformed under appropriate central windowing (we used triangular kernels with a 10% smoothing around the central frequency); the average spectra relative to the whole duration of each measurement are then computed for the vertical and horizontal components, the two horizontal ones are square-root diagonally summed and the HVSR computed. Each seismic noise acquisition lasted 15–20 min and was repeated at different times of the day and at different days of the week, finding large differences in absolute noise amplitude on the single components, but a good stability of the HVSR curve (Fig. 3). A similar stability was observed (Fig. 4) under the seasonal variation of meteorological conditions, which are the known main factor affecting seismic noise amplitude (e.g., Hasselmann, 1963; Friedrich et al., 1998); these were studied through two series of continuous HVSR measurements completed in August 2015, respectively centered in spring and autumn, when the largest meteorological changes occur. Such measurements were performed at the supposedly most critical site – Chioggia – which is located above the deepest bedrock interface of the Po Valley – and at a site in Venice mainland, where the bedrock interface is slightly shallower (blue dot in Fig. 1). A modest dependence of the HVSR curve on time of the day and season was found, with
Fig. 2. The HVSR signal relative to the whole signal recorded during the whole year 2008 by the Streckeisen STS-2 broadband seismograph of the MedNet SLNM-TRI station (from Fig. 10 of Castellaro and Mulargia, 2012).
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 3. The vertical (top panel) and horizontal component (mid panel) of noise spectral amplitude measured in good and foul weather, showing large variations between the two conditions. Conversely, the HVSR for the same data (bottom panel) remains remarkably stable, providing essentially the same picture. In this panel thick lines represent the average values, thin lines the 2r confidence bands (cf. Fig. 1 of Castellaro, in press).
the HVSR shape always clearly apparent down to the lower frequency HVSR peak, which is the marker of the deep sedimentbedrock interface (Fig. 4). 2.3. HVSR subsoil mapping: the procedure If one assumes that no strong lateral discontinuity is present near the observation point, a local 1-D approximation can be used. This allows to largely simplify the calculations reducing them to evaluate the eigenfunctions of the Rayleigh equation (see e.g., Haskell, 1953; Knopoff, 1964), and is also consistent with the first-order approximate nature of the present approach. The problem is thus reduced to 1-D modal summation, which provides directly the wavefield in the spectral form required to evaluate HVSR. Note how this, in a 2-D (or 3-D) treatment, coincides with a body wave assumption of noise composition. Obviously, due to the interference of P and SV waves, at some distance from the source this evolves in a surface Rayleigh wave approach. The Knop-
off–Schwab expansion (Schwab et al., 1984) was shown to be free of numerical instabilities up to frequencies well beyond those involved by the size of the scatterers in the Earth’s crust (e.g., Ben-Menahem and Singh, 1981). Several specific formulations are available for calculating the 1-D coupling coefficients (e.g., BenMenahem and Singh, 1981). To effectively treat all numerical stability problems, we used the latter with Dunkin (1965) stability correction, extending summation up to the first five Rayleigh modes, since these were found to account for virtually all energy transmission (cf. Mulargia and Castellaro, 2013). A variety of behaviors has been suggested for the anelastic wave scattering parameter Q (Ben-Menahem and Singh, 1981; Seale and Archuleta, 1989; Fukushima et al., 1992; Olsen et al., 2003; : Assimaki et al., 2005; Satoh, 2006), with values Q ’ 3¼15 for the : soil and Q ’ 50¼120 for the superficial bedrock, with a mild frequency dependence. According to our passive array measurement technique of both elastic and anelastic parameters (Mulargia and Castellaro, 2013), Q was measured in the Southern part of the same
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 4. The results of a series of HVSR measurements completed in August 2015: panel A shows the Dec–Mar monthly averaged HVSR at Chioggia, while panel B shows the same in May–Aug at the site where also the array measurements were taken (blue dot in Fig. 1). In both pictures the thick line is the HVSR curve and the thin ones are the 2r confidence bands.
: sedimentary basin, obtaining Q ’ 10¼12 at shallow depth, and an approximate independence of frequency (ibid.). In the synthetic fit, we therefore simply took Q ¼ 10 for soil, and Q ¼ 100 for the bedrock, independent of frequency (cf. also Malagnini, 1996). The synthetic HVSR curve is linked to impedance contrasts between successive strata and provides per se a subsoil image in terms of frequency. Translating this into a stratigraphic image in depth requires, as we already noted, an independent calibration. A simple and practically effective approach (Castellaro and Mulargia, 2009) is to use as a constraint one of the shallow depth interfaces which is independently known from direct geotechnical surveys (drilling, penetration tests, outcrops, trenches, construction works, etc.) or the near surface Vp and Vs profiles inferred from passive or active array surveys. It is important to note that our fit considers both the frequency and the amplitude of the HVSR peaks, with the latter depending primarily on the S and P wave impedance contrasts, but being also inversely proportional to the spectral smoothing function. The latter is subjectively chosen as a compromise between resolution and stability (cf. e.g., Konno and Ohmachi, 1998) and therefore the peak height does not represent an absolute quantity, but the relative amplitude of the contrasts.
3. Results and discussion The results of the analysis of the recordings on the transect is shown in Fig. 5. In all cases the measurements provided clear markers for the bedrock interface, which was always apparent at frequencies below 0.5 Hz. Comparing the map produced by the HVSR fit with the one independently produced by well data, a good match was obtained along the 7 station transect (Fig. 5). Namely, using as a constraint the near surface Vs profile derived by an independent passive array study (Mulargia and Castellaro, 2008), the
HVSR fit was capable to resolve correctly both a major gravelly bank at 0.2 km depth and the non-horizontal dolomitic bedrock sloping from 0.5 km depth in the N-E part to more than 1.1 km in the SW part, with a dip angle larger than 1 . This suggests that even when the wavelengths involved are comparable with the transect length (about 30 km), modest bedrock slopes can be properly resolved by the 1-D local modes (Ben-Hador and Buchen, 1999). Similarly clear images were obtained at Chioggia (Fig. 6), where the HVSR constrained fit gave a depth estimate of about 1.6 km, and where the surface interpolated from exploration data (Fig. 1) place the bedrock at a depth of 1.8 km. The marker of this deep interface is a HVSR peak at about 0.25 Hz, which was always clearly apparent, with a marginal influence of the daily and seasonal variations in noise amplitude (see Fig. 4). Summing up, quarter of an hour seismic noise HVSR recordings allowed to consistently resolve the major stratigraphic subsoil discontinuities down to the 2 km (see Figs. 6 and 1) depths reached by the sediment Pre-Plio-Pleistocene bedrock interface at the basis of the Po Plane Valley. While this may sound unimpressive, given that passive multi-station seismic imaging has been successfully applied down to the earth’s mantle (e.g., Pavlis, 2003) and core (e.g., Lin et al., 2013), such results are in a completely different category than HVSR. In fact, their imaging capability depends crucially on the level of passive illumination, i.e., since the origin of seismic noise is essentially the nonlinear interaction among ocean waves and among them and the ocean bottom, it is tied to sea storms, and is therefore largely variable. Such passive imaging techniques require to ‘‘capture the signal” with large arrays and long observation times, waiting months for huge storms to provide a sufficient illumination. HVSR noise subsoil mapping is also a passive imaging technique, but a very different one: first of all, it operates at higher frequencies, since below 0:1 Hz, due to ground tilt, HVSR becomes unreliable. This limits the HVSR subsoil mapping range to the first very few kilometers, but the normalization of the hori-
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
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Fig. 5. The V s subsoil image inferred from the HVSR synthetic fit along the transect A-B (see Fig. 1). The black line illustrates the bedrock depth as per the AGIP (1988) map. The apparent bifurcation of the 1.2 km/s region below 0.9 km depth around the middle of the transect is an artefact of the contour mapping.
Fig. 6. Imaging the V s profile vs depth from the HVSR synthetic fit of a 20’ HVSR recording at Chioggia (see Fig. 1). The bedrock depth is estimated at 1.6 km.
zontal spectrum by the vertical one leads to analyze a signal which has little in common with the single spectrum of the vertical motion, generally used in deep seismic imaging. As we already said, the normalization makes the HVSR nearly independent of absolute noise level and cancels out the effect of sources, sorting the effect of local subsoil structure. Beyond depth, the other major drawback of HVSR is its intrinsically approximate nature, which limits velocity and depth estimates to inaccuracies of 25%. However, these may be not much worse than those inherent to the spatial interpolation of exploration data, like it possibly happens also in the present case. 4. Conclusions The goal of the present study was to ascertain the deep stratigraphic mapping capability of HVSR, a tool so far ubiquitous in seismic microzonation for its inexpensive and reliable capability
to measure the elastic response of shallow subsoil. Since the latter response, when combined with an independent constraint as the shallow depth velocity profile or any known interface, can be translated into a stratigraphic image, HVSR is also a subsoil imaging tool, already used with success at shallow depths. We have experimentally verified that such a mapping capability extends to the few kilometer depths of geological and geophysical exploration interest. Namely, we have found that, with 15–20 min single-station measurements, HVSR is capable to resolve the major stratigraphic interfaces of the Po Plane Valley, Italy, down to depths of the order of 2 km. HVSR allowed to effectively resolve impedance contrasts around 50%, and therefore the synthetic fit is in principle able to identify not only sediment-bedrock interfaces but also soil-soil and rock-rock ones. Finally, we found that mild deviations from planarity can be readily resolved. Summing up, the positive results obtained stand in favor of HVSR as a tool for fast, low-cost, first-order subsoil mapping down
Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002
F. Mulargia, S. Castellaro / Physics of the Earth and Planetary Interiors xxx (2016) xxx–xxx
to the kilometer range. Only interfaces with a larger than 50% impedance contrast can be resolved, and the uncertainties in the depth estimates are tied to the 25% uncertainties in the velocity estimates and to the constraint – interface depth difference. If the constraint is superficial, the depth uncertainties are also about 25%, but if the constraint has a depth comparable with the interface, measured in the same region by a well or by other independent survey, they can be substantially smaller. In our case, by using only near surface constraints, the deep Po Plane Valley interface at Chioggia is estimated at 1.6 km, while independent exploration data set it at 1:8 km. HVSR subsoil depth mapping may regard a variety of practical applications, including geologic modeling, large scale preliminary surveying for resources, areal extension of surveys performed with standard methods (like, for example, mapping the interface topography around a well), independent cross-checking of previous surveys, etc. The technique appears to have a strong practical potential especially at the larger depths it can reach: while survey costs with standard techniques increase at least linearly with depth, HVSR, requiring neither to drill boreholes nor to deploy long arrays for seismic exploration – and not even to use longer observation times – remains identically inexpensive.
Acknowledgements This work was completed with a RFO contribution from the Università di Bologna.
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Please cite this article in press as: Mulargia, F., Castellaro, S. HVSR deep mapping tested down to 1.8 km in Po Plane Valley, Italy. Phys. Earth Planet. In. (2016), http://dx.doi.org/10.1016/j.pepi.2016.08.002