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Seismicity and crustal structure beneath the western Ligurian Sea derived from local earthquake tomography
Tectonophysics 339 (2001) 495±510
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Seismicity and crustal structure beneath the western Ligurian Sea derived from local earthquake tomography E. Eva a, S. Solarino b,*, D. Spallarossa a a
INGV Istituto Nazionale di Geo®sica e Vulcanologia, c/o Dipartimento per lo Studio del Territorio e delle sue Risorse, UniversitaÁ di Genova, Genova, Italy b Dipartimento per lo Studio del Territorio e delle sue Risorse, UniversitaÁ di Genova, Viale Benedetto XV, 5, 16132 Genova, Italy Received 15 March 2000; accepted 17 April 2001
Abstract In this paper, we present and comment on the results of a tomographic inversion of P arrival times of local earthquakes to better understand the structure and features of the Ligurian Sea, an oceanic basin originated in the Oligocene±Miocene. This tomographic inversion is the last step in a long and careful revision of the data available for the Ligurian Sea. An accurate catalogue derived from a controlled compilation of data from the numerous stations monitoring seismic activity in this young oceanic basin has been used for computation of a one dimensional (1D) reference model. A high-quality subset of the new catalogue has been used for the non-linear 3D tomographic inversion by iteratively solving the coupled hypocenter-velocity problem in a least square sense. Careful analysis of the resolution capability of the used data set has revealed the better-resolved features for interpretation. The resulting 3D model shows a high-velocity layer extending from the northeastern side of the model, where it lays about 30 km deep, to the southwestern part where it shallows to 15 km. The shallow part of this high-velocity body is located near the original area of the opening of the Ligurian Sea that took place between the Oligocene and early Miocene. Its velocity is comparable with that of an oceanic Moho (around 7.8 km/s). A lens-shaped high-velocity body, about 25 km long, located at a depth of 8±15 km, is interpreted as a series of ophiolitic bodies intruded into the upper crust. q 2001 Elsevier Science B.V. All rights reserved. Keywords: Ligurian Sea; tomography; velocity perturbation; Moho
1. Introduction The tectonic features and geodynamics of the Ligurian Sea have been the focus of many studies using different techniques (e.g. Azuende et al., 1973; Burrus, 1984; ReÂhault et al., 1984; Augliera et al., 1994; Makris et al., 1999). In the following, we brie¯y * Corresponding author. Tel.: 139-10-353-8086; fax: 139-10353-8081. E-mail address: [email protected] (S. Solarino).
summarize the principal results to introduce the topic of this paper and to document the need for a revision of the seismic catalogues obtained from the permanent station network in the region (Fig. 1). The Ligurian Sea originated in the counterclockwise rotation, of around 308, of the Corsican± Sardinian block relative to the European plate, due to the convergence of the European and African plates. The tectonic characteristics of this basin are a very shallow Moho, with a depth of only about 20 km (Giese and Buness, 1992; Ginzburg et al.,
0040-1951/01/$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0040-195 1(01)00106-8
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Fig. 1. Distribution of seismic stations around the Ligurian Sea and of OBS. Not all stations were operating at the same time.
1986), a very steep NW margin incised with canyons, and the presence of a system of shallow normal faults parallel to the coast (Chaumillon et al., 1994). The presence of an oceanic crust is documented by refraction data (Recq et al., 1976), revised by De Voogd et al. (1991), and con®rmed by gravity modeling (Klingele et al., 1992) and magnetic anomalies interpretation (Wonik et al., 1992). The tectonic features would seem to con®rm the tensional origin of the basin, but recent seismological evidence has led to a converse opinion: the two major recent earthquakes in the Ligurian Sea (M 6:0; 19 July 1963 earthquakes) show compressive focal mechanisms and the recent computation of the stress ®eld shows the principal horizontal stress vector with a direction NW±SE (Eva and Solarino, 1998).
Furthermore, in a recent paper, Bethoux et al. (1992) suggest that the Ligurian Sea is actually closing and a compression, that would justify the occurrence of the above-mentioned compressional events, is being reactivated. The historically and instrumentally determined seismicity of the Ligurian Sea (Fig. 2) is principally concentrated in the western part and localized at the foot of the continental margin. The seismic activity is mostly con®ned to events of magnitude up to 5.0; few events reach larger magnitudes (M 6:0; 19 July 1963 earthquakes and estimated M 6:0±6:2; 23 February 1887 (Capponi et al., 1980)). Seismic activity is also present offshore but it is neither consistent nor continuous. In particular, there is a lack of seismicity that seems to be almost coincident with a high
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Fig. 2. Seismicity (Period 1981±1998) of the western Alpine chain. The framed area is the object of this study. Solid lines represent the surface heat ¯ux as derived by Pasquale et al. (1996).
heat-¯ux zone outlined by Pasquale et al. (1994, 1996). In this context, it is necessary to better investigate the seismicity and the seismotectonics of this part of the Ligurian Sea. Bethoux et al. (1992) revised seismicity according to one dimensional velocity propagation model. As a main conclusion, they underlined the complexity of the Moho geometry, con®rmed also by Pasquale et al. (1996). This paper is mainly
concerned with a detailed tomographic reconstruction of the area to better de®ne the Moho geometry. The paper is organized as follows: a description of the source data; the selection of high-quality subsets, computation and validation of a 1D reference velocity propagation model; use of the 1D model as initial reference model for the 3D tomographic imaging of the crust using local earthquake data; revision of earthquake locations.
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2. Earthquakes catalogue Our ®rst goal was to collect, reformat and clean a large data set for the Ligurian Sea, consisting of all data available for the period 1982±1999. Several seismic networks [Renass (Strasbourg), Sismalp (Grenoble), LDG (Paris), SSS (ZuÈrich), ING (Rome), RSNI (Regional Seimic Network of northwestern Italy, Genova)] have been operating since the beginning of the 80s in an area encompassing the western Alps and the Ligurian Sea. The long recording period of these permanent networks and several temporary seismic station networks provided a comparatively large data set for an area of moderate seismic activity if all the data were merged. Exchange of data is a real necessity for those seismic events that, occurring between the networks, cannot be precisely located by any single network. For a long time, data have routinely been exchanged more generally to provide the best possible station coverage for locations. It is worth noting that the geographical distribution of the stations in the area under study is very uneven, especially in the southern part. Fig. 1 shows stations that are currently in operation or were operating temporarily in the area and were made available for this work. The map clearly shows the limits and constraints that such a station distribution imposes on location quality. From a theoretical point of view, merging travel time data is a simple and straightforward procedure. In practice, however, different ways of recording and treating the data may result in several sources of potential errors if the merging does not include a careful revision of all available (and sometimes unavailable) information. Bearing this in mind, we reviewed all data included in the catalogue to ensure the reliability of the merging process and to have as a complete catalogue as we could. Picking errors, wrong phase recognition and mistaken station coordinates can strongly bias the quality of locations and the whole catalogue. The revision process was conducted following the procedure described in Solarino et al. (1997) and consisted of several steps, including: ² check and revision of all stations names and coordinates; ² revision of merged arrival times;
² recognition and erasure of doubled and unreliable readings. The resulting data set was relocated with Hypoellipse program (Lahr, 1979) using different velocity propagation models for different groups of stations, in order to reduce the effects of strong lateral heterogeneities. Fig. 2 shows the distribution of seismicity for the whole data set, while the framed area includes the seismicity (more than 10 000 events) which was used as a basis for this study. 3. Computation of 1D reference model Several papers (among others, Kissling et al., 1994, 1995) underline the importance of a 1D reference model for both location and tomographic purposes. Even if we were dubious that a single 1D velocity propagation model, that is only a crude approximation of the real earth, could adequately represent the structural complexity of the Ligurian Sea for modeling purposes, we performed the computation of such a model as it is necessary to obtain a reliable basis for the computation of the 3D propagation model. The calculation of the propagation model is the result of a combined, simultaneous inversion of a large number of selected high-quality events for the 1D velocity model parameters and the hypocenter locations. Different models with about the same residual variance and location precision can usually be obtained from the same data set. The model that coincides best with the surface geology and a priori information on the near-surface structure has to be chosen as the ®nal model, and will be called `minimum' (Kissling, 1988) to indicate that it provides minimum average (RMS) values for all earthquakes used in the inversion. The calculation of a minimum 1D model in conjunction with the checking and selection of the earthquake data is a tedious procedure that can be speeded-up if a great number of starting constrains are applied. This means, for example, that use of a propagation model obtained by other means (refraction seismic pro®les) or for other purposes (a 1D model for a larger or neighboring area) as input could dramatically reduce computation time. In fact in these cases all that needs to be done is to `adapt' the existing model to the new geometry and station
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Fig. 3. Distribution of events and stations for KS approach (top panel) and for EGT approach (bottom panel). The area for EGT approach has a corner cut to avoid Alpine seismicity being too in¯uential (see text).
setting. We decided to use VELEST program (Ellsworth, 1977; Kissling, 1988, 1995) to compute the 1D model because of the high-¯exibility offered by the software. To reduce computation time, considering that a 1D
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model is only the ®rst step in the inversion procedure, we approached the problem using two different models (and data sets) already partly tuned to the area, one based entirely on refraction seismology (EGT in the following, Buness et al., 1989) and one obtained by inversion of local earthquake data (Kissling et al., 1995, KS in the following). Neither of the models is currently used for routine locations at RSNI; location parameters are, in fact, obtained with Hypo techniques (Lahr, 1979) using several velocity models for different groups of seismic stations. This should take into account the various domains (Alps, Ligurian Sea, Apennines) and have `alpine', `apenninic' and `oceanic' models for local seismicity, inside the respective areas. As it is not feasible to merge these models to obtain a unique 1D propagation model, an `averaged' velocity model must be computed. Identical and restrictive criteria have been adopted to select two data sets to be used for computation: We established at least 6 P readings as selecting threshold and a maximum azimuthal gap of 1808. To avoid an overly strong Alpine seismicity in¯uence and to better focus on in-homogeneities and tectonic features of the Ligurian Sea only, additional selection criteria were introduced. In one case (the KS data set) the selection was performed on a squared area, ®ltering the data through a grid-like selection in which only a dozen events were selected per grid square. In the other case (the EGT data set), the northwestern edge of the area was cut off to partially exclude the Alpine seismicity. Fig. 3 reports the adopted geometry and the selected seismicity for KS (top panel) and EGT (bottom panel). Finally, the in¯uence of indirect rays was avoided by selecting only those readings that could be considered ®rst arriving diffracted P phases (recorded less than 100 km from the epicenter) for each event. This is not necessary for the computation of the 1D model with VELEST, but it is necessary to ful®ll the requirements of the approximate ray tracing (ART) used by the 3D inversion code SIMULPS (described in Section 4). The ®nal result of such a comprehensive selection is two different data sets with some common events, one made of 1135 events (KS), the other of 1365 (EGT) recorded by several stations inside and outside the respective areas.
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Fig. 4. Top panels: left, ®nal station corrections for model resulting from 1D computation using KS; right, 1D P velocity initial (solid line) and ®nal (broken line) propagation model. Bottom panel: as above starting from EGT model.
Fig. 4 shows the results of the two computations: initial and ®nal 1D P models are reported with the corresponding station delays. To better understand the performance of the newly computed 1D models, they were used for the relocation of a third, independent data set (cross-validation technique) made of medium-high quality events partly coinciding with those used for the model calculation (Fig. 5). Fig. 6 reports a comparison of the focal parameters and RMS
of the two resulting locations for this data set, applying VELEST in a single event mode (solution of the lonely location problem). Hypocentral and depth differences are of a low order (at most about 0.068, 7 km if latitude is taken into account); the only systematic trend evident is for origin time due to a greater average velocity for shallow layers in the KS model. Since the RMS values have been computed using the same data set and method, the differences
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Fig. 5. Main characteristics of the data set used for estimating quality of propagation model (cross validation technique, see text). Epicentral positions, number of P phases (top), depth distribution and gap (bottom).
in their distributions (bottom panels of Fig. 6, KS left, EGT right) can be attributed only to the capability of the 1D model to ®t the travel time observations. In this sense, the RMS distribution is a helpful tool in choosing a 1D model. Due to the great variability of the Moho under the Ligurian Sea, and especially under the transition zone between the sea and the land, we did not expect a new 1D model to be really able to account for the Moho topography. Therefore, we then proceeded to the next step, using the KS 1D model, to obtain better constraints (locations) from the 3D velocity propagation model. 4. Local tomography and 3D modeling For the 3D inversion, we used the SIMULPS code originally written by Thurber (1983), which performs the inversion using a damped least squares approach and implements a grid-parameterization of the
velocity model, where velocity values are de®ned at grid-nodes and are linearly interpolated between nodes. To solve the forward problem, approximate 3D ray tracing and pseudo bending were applied. Hypocenter locations were updated within the new velocity model at each iteration step. Since the 1D model resulting from the inversion of the KS model turned out to be best, it was used as initial reference model for the tomography, after proper re-parameterization accounting for the main difference between the two inversion schemes. In fact, the output from VELEST is a layered model with constant layer velocities, while the input 1D model for SIMULPS is a gradient model. The choice of suitable damping was made by constructing a trade-off curve (see Fig. 7) and selecting the value that most reduced the data variance without causing a large increase in the solution variance (Eberhart-Philips, 1986). The trade-off curve was computed using a large range of damping values, performing oneiteration inversions. Because of the shape of the curve,
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Fig. 6. Comparison between location parameters adopting the two 1D models (KS minus EGT). (A) latitude, (B) longitude, (C) origin time, (D) depth, (E) and (F) RMS for KS and EGT, respectively.
it was evident that there was a large range of values (300±600) that responded to the goal of reducing data variance without increasing solution variance. Since we did not want to overdamp too much, a value of 300 was chosen (see arrow in Fig. 7). The ®nal 3D model was obtained after seven iterations. To test the improvement introduced by the newly computed 3D model, the independent data set was used for relocation and comparison. The most
important error-estimates (ERH, ERZ, RMS) of the ®nal locations were compared to those obtained using the 1D model and displayed in a comprehensive set of charts (Fig. 8). The use of an appropriate 3D model increased the quality of the determination of focal parameters, consistently but not very much, since the improvement was computed with respect to locations that already represented the `best' obtainable using a 1D model.
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Fig. 7. Trade-off curve for selecting the optimum damping for the P data set. A wide range of values ensures reduction of data variance without causing a large increase in the solution variance. The shape of the curve is probably due to the non-homogeneity of the data set.
An aerial view of the resulting 3D structure is reported in Fig. 9, where we plotted percentage velocities with respect to the average velocity computed over sample nodes for each layer at depths of 3, 10, 15, 20, 25 and 30 km. Only those areas, in which the sampling was greater than 10, have been reported. As we are much more interested in deeper structures, and considering that shallow layers are much more biased by uneven ray distribution (due
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to station distribution), special attention was devoted to inversion results for deep layers. A careful analysis of reliability must be conducted before any interpretation of tomographic results can be made. In fact, any tomographic image is only as good as its resolution estimates. When assessing the quality of the tomographic inversion, one must determine how well each node was illuminated (number of rays hitting the node) and how well it was resolved. The very rough estimate of illumination of the model contained in the hit count matrix (reporting the number of rays that contribute to the solution of that node) is supported by the derivative weight sum (DWS, Fig. 10), a more sensitive measurement of the spatial sampling of the model space. It quanti®es the relative ray density in the volume of in¯uence of a model node, weighing the importance of each ray segment by its distance from the model node (Haslinger et al., 1999). The best and quickest estimate of the quality of the solution is provided by the resolution diagonal element (RDE, Fig. 11). It is a diagnostic tool which shows the degree of independence of one model
Fig. 8. Comparison of 1D (top) and 3D (bottom) locations for KS model. Use of a 3D model slightly increases the quality of (already good) locations.
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Fig. 9. Aerial views of tomographic results. Grey is for unsampled areas. The solid white contours depict reliably resolved areas. Velocity percentages are computed with respect to the average velocity over all sampled nodes. FT: Furia-Taggia Canyon; Var: Var Canyon; IB: Ivrea Body.
parameter in the solution by showing the diagonal element of the full solution matrix; it ranges from 0.0 to 1.0 and the larger it is, the more independent the solution is.
A consistent problem is how to use these diagnostic tools once they are computed. One should ®x reliability limits that tend to have good sampling, high values of RDE and DWS, for a certain area. Haslinger (1998)
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Fig. 10. Derivative weight sum values for the planes shown in Fig. 9.
suggested that not even synthetic tests conducted on a ®xed model geometry and data set could quantitatively determine the choice of upper and lower reliability bounds. The choice is therefore somewhat arbitrary. Because of geographical constraints we
could not be very conservative in this work, and we decided to consider all areas in which RDE was greater than 0.1 and DWS greater than 100 as fairly resolved. A white contour line circumscribes the areas where the quality requirements are ful®lled, and
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Fig. 11. Resolution diagonal element values for the planes shown in Fig. 9.
everything lying inside is taken into account in the description, interpretation and discussion of the results. Looking at the aerial views, all layers show strong lateral velocity variations, reaching maximum values of more than 15%: nevertheless, because of the
spot-like images (high frequency spatial anomalies) and the complexity of the tectonic setting of the area investigated, it would appear to be dif®cult to obtain a unique and complete interpretation of the tomographic results. We could distinguish between two different anomaly
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Fig. 12. Positions of the NNW±SSE (left) and NE±SW (right) tomographic cross sections shown in Fig. 13.
patterns in the shallow (3, 10 km) and deeper layers (15, 20, 25, 30 km). Velocity anomalies in the shallow layers are mainly related to the outcropping geological structures. The northern part of the model is characterized by two high-velocity anomalies, both extending to a depth of 20 km, one located east of the Argentera Massif the other located in the NE corner of the model. The ®rst anomaly could be related to the southern part of the Ivrea body, as proposed by previous tomographic studies (Solarino et al., 1997). This complex of high-density, high-velocity and high-susceptibily rocks is supposed to extend to a depth of more than 30 km in the northern part (close to the Ivrea-Verbano area, where it outcrops) and to ¯oat in the southern part. Our results con®rm such behavior, at least for the part included in our tomographic image. In fact, we do not see a high-velocity body below 20 km of depth. The other velocity perturbation can be related to the magnetic anomalies investigated in previous studies (Froidevaux and Guillaume, 1979; Bozzo et al., 1986; Bozzo et al., 1992) but never con®rmed by tomographic results. An extended high-velocity anomaly located in the SW corner of the model is the main feature of the deeper layers, in contrast with a low-velocity anomaly at all depths in the northeastern sector of the Ligurian Sea. 5. Discussion and conclusions A few tomographic cross-sections, reporting
absolute velocity values, are displayed in Figs. 12 and 13. In particular, Fig. 12 shows three crosssections oriented NW±SE, while Fig. 13 displays cross-sections parallel to the coast. In the following, we make use of these to try to comment on the tectonic setting of the complex Ligurian Sea area. The high-velocity body, strongly related to a concentrated and consistent seismic activity, already demonstrated by the analysis of tomographic maps (see previous paragraph) is also clearly evident in the AB cross-section (Fig. 12). It appears as a 25 km long, lens-shaped body located at a depth of 8±13 km. On the basis of gravimetric and magnetic observations, respectively, Froidevaux and Guillaume (1979) and Bozzo et al. (1992) interpreted this anomaly as the existence of ophiolitic bodies seated deep in the upper crust. A low velocity zone is also clearly evident in the sections perpendicular and parallel to the coast: in the CD cross-section low velocities are displayed to a depth of 3 km, extending from the coast to the foot of the continental margin. Even if the reliability of the tomographic image of this low velocity area is questionable, this anomaly can be interpreted as evidence of the very thick layers of sediments, at least for the ®rst kilometers, related to the presence of two deep, active canyons oriented perpendicular to the coast (canyons of Furia-Taggia and Var) (Chaumillon et al., 1994). The deeper, low velocity, layer clearly evident in the northeastern part of the tomographic model (see cross-sections GH and IL), indicates a
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Fig. 13. Tomographic cross sections in absolute velocity. AB, CD and EF are almost perpendicular to the coast while GH and IL are parallel to it (see Fig. 12).
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strong crustal thickening up to 25 km deep that could be related to the triple junction between the Alpine, Apenninic and Ligurian structures. In particular, a crustal indenter could be hypothesized in an area where the principal tectonic elements have their rotation point and the two principal plates are pushing one each other. Assuming a velocity of 7.8 km/s for the crust±mantle boundary (Bethoux et al., 1986), in cross-sections GH and EF, the depth of the Moho appears strongly variable. In the northern and eastern part of the tomographic model, the depth of the Moho is about 30 km, deepening to 36 km, in the central part of the CD cross-section, and shallowing to 15 km in the southwestern part of the imaged model (crosssection GH). Even if these results roughly con®rm the position of the ocean boundaries predicted by several authors, a number of questions still remain. In particular, the studies of the opening of the Ligurian Sea, on the basis of heat ¯ow (Pasquale et al., 1996), gravimetric (Morelli et al., 1977; Klingele et al., 1992), magnetic (Galdeano and Rossignol, 1977; Wonik et al., 1992) and wide-angle seismic pro®le interpretation (Makris et al., 1999), have led to the hypothesis of the presence of an oceanic crust in the bathyal plain of the Ligurian basin. Actually, because of the scarce tomographic resolution in the central part of the Ligurian Sea (see thick white line in Fig. 9), this hypothesis cannot be supported by the reconstructed images. On the other hand, in crosssections GH and IL (north of the hypothesized oceanic Moho of other authors), the transition between the oceanic realm (southwestern side of the crosssections) and the continental part of the Ligurian Sea is also well de®ned by the behavior of the layer, with a velocity of more then 7.0 km/s. P-wave velocities ranging from 7.0 to 7.7 km/s are typical of the 3B layer of an oceanic crust and indicate a cumulatericher gabbro layer at the base of the crust. Moreover, the pattern of the re-located seismicity (see top panel of Figs. 12 and 13) shows that seismic activity is mainly concentrated on the borders of the high velocity zone, while it disappears within it. Acknowledgements We wish to thank Edi Kissling and an anonymous
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reviewer for providing us not only with a constructive reviews but also with useful ideas for further investigations and additional references. This paper would not have been possible without the daily efforts of all the staff of the Genova seismic network. We thank them all. This work was supported by the DEVINE Interreg II Project. References Augliera, P., Bethoux, N., Deverchere, J., Eva, C., 1994. The Ligurian Sea: new seismotectonic evidence. Boll Geof. Teor. Appl., XXXVI, 363±380. Azuende, J.M., Bonnin, J., Olivet, J.L., 1973. The origin of the western Mediterranean basin. Geol. Soc. London 129, 607±620. Bethoux, N., Petit, F., Rehault, J.P., Massinon, B., Montagner, J.P., 1986. Several location methods for underwater shots in the Gulf of Genoa (Western Mediterranean): structural implications. Tectonophysics 128, 357±379. Bethoux, N., FreÂchet, J., Guyoton, F., Thouvenot, F., Cattaneo, M., Eva, C., Nicolas, M., Granet, M., 1992. A closing Ligurian Sea? Pure Appl. Geophys. 139, 179±194. Bozzo, E., Cattaneo, M., Giglia, G., Pegoraro, G., 1986. Deep magnetic structures in the Piedmontese Tertiary Basin (Ligurian±Piedmontese area, Northern Italy). Boll. Geof. Teor. Appl., XXVIII 109, 49±63. Bozzo, E., Campi, S., Capponi, G., Giglia, G., 1992. The suture between the Alps and Apennines in the Ligurian sector based on geological and geomagnetic data. Tectonophysics 206, 159±169. Buness, H., Giese, O., Hirn, A., Scarascia, S., 1989. Crustal structure derived from siesmic refraction between the Southern Alps and the Ligurian Sea. In: Freemen, R., Mueller, St. (Eds.), Proc. 6th Workshop on the European Geotraverse (EGT) Project, pp. 165±167. Burrus, J., 1984. Contribution to a geodynamic synthesis of the Provencal basin (north western Mediterranean). Mar. Geol. 55, 247±270. Capponi, G., Eva, C., Merlanti, F., 1980. Il terremoto del 23 Febbraio 1887 in Liguria occidentale. Atti Acad. Ligure Sci. Lett. 37, 1±33. Chaumillon, E., DevercheÁre, J., ReÂhault, J.P., Gueguen, E., 1994. ReÂactivation tectonique et ¯exure continentale Ligure (MeÂditerraneÂe Occidentale). C. R. Acad. Paris 319, 675±682. De Voogd, B., Nicolich, R., Olivet, J.L., Fanucci, F., Burrus, J., Mauffret, A., Pascal, G., Arniani, A., Auzende, J.M., Bernabini, M., Bois, C., Carmignani, L., Fabbri, A., Finetti, I., Galdeano, A., Gorini, C.Y., Labaume, P., Lajat, D., Patriat, P., Pinet, B., Ravat, J., Ricci Lucchi, F., Vernassa, S., 1991. First deep seismic re¯ection transect from the Gulf of Lions to Sardinia (ECORS-CROP pro®le in Western Mediterranean). In: Meissner, R., Brown, L., Durbaume, H.J., Franke, W., Fuchs, K., Seifert, F. (Eds.), Continental Lithosphere: Deep Seismic Re¯ection, Am. Geophys. Union, Geodyn. Ser. 22, 265±274.
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KlingeleÂ, E., Lahmeyer, B., Freeman, R., 1992. Bouguer gravity anomalies. In: Blundell, D., Freeman, R., Mueller, St. (Eds.), A Continental Revealed: The European Geotraverse. Cambridge University Press, Cambridge, UK, pp. 27±30. Lahr, J.C., 1979. Hypoellipse: a computer program for determining local earthquake hypocentral parameters, magnitude and ®rst motion patterns. U.S. Geol. Surv., Open-File Report 79-431. Makris, J., Egloff, F., Nicolich, R., Rihm, R., 1999. Crustal structure from the Ligurian sea to the Northern Apennines Ð a wide angle seismic transect. Tectonophysics 301, 305±319. Morelli, C., Giese, P., Carrozzo, M.T., Colombi, B., Guerra, I., Hirn, A., Letz, A., Nicolich, R., Prodehl, C., Reichert, C., Rower, M., Scarascia, S., Wigger, P., 1977. Crustal and upper Mantle of the Northern Apennines, the Ligurian Sea, and Corsica, derived from seismic and gravimetric data. Boll. Geo®s. Teor. Appl. 75±76, 199±260. Pasquale, V., Verdoya, M., Chiozzi, P., 1994. Types of crust beneath the Ligurian Sea. Terra Nova 6, 255±266. Pasquale, V., Verdoya, M., Chiozzi, P., Ranalli, G., 1996. Rheology and seismotectonic regime in the northern central Mediterranean. Tectonophysics 270, 239±257. Recq, M., ReÂhault, J-P., Bellaiche, G., GeÂnesseaux, M., Steve, J-P., 1976. UniteÂs structurales de le marge continentale sous-marine de Cannes aÁ Menton d'apreÁs la sismique reÂfraction. Earth Planet. Sci. Lett. 28, 323±330. ReÂhault, J-P., Boillot, G., Mauffret, A., 1984. The western Mediterranean basin geological evolution. Mar. Geol. 55, 429±445. Solarino, S., Kissling, E., Cattaneo, M., Eva, C., 1997. Local earthquake tomography of the Southern part of the Ivrea body, North-Western Italy. Eclogae Geol. Helv. 90, 357±364. Thurber, C.H., 1983. Earthquake locations and three dimensional crustal regions velocity structure in the Coyote Lake area, central California. J. Geophys. Res. 88, 8226±8236. Wonik, T., GaldeÂano, A., Hahn, A., Mouge, P., 1992. Magnetic anomalies. In: Blundell, D., Freeman, R., Mueller, St. (Eds.), A Continent Revealed: The European Geotraverse. Cambridge University Press, Cambridge, UK, pp. 31±34.
www.elsevier.com/locate/tecto
Seismicity and crustal structure beneath the western Ligurian Sea derived from local earthquake tomography E. Eva a, S. Solarino b,*, D. Spallarossa a a
INGV Istituto Nazionale di Geo®sica e Vulcanologia, c/o Dipartimento per lo Studio del Territorio e delle sue Risorse, UniversitaÁ di Genova, Genova, Italy b Dipartimento per lo Studio del Territorio e delle sue Risorse, UniversitaÁ di Genova, Viale Benedetto XV, 5, 16132 Genova, Italy Received 15 March 2000; accepted 17 April 2001
Abstract In this paper, we present and comment on the results of a tomographic inversion of P arrival times of local earthquakes to better understand the structure and features of the Ligurian Sea, an oceanic basin originated in the Oligocene±Miocene. This tomographic inversion is the last step in a long and careful revision of the data available for the Ligurian Sea. An accurate catalogue derived from a controlled compilation of data from the numerous stations monitoring seismic activity in this young oceanic basin has been used for computation of a one dimensional (1D) reference model. A high-quality subset of the new catalogue has been used for the non-linear 3D tomographic inversion by iteratively solving the coupled hypocenter-velocity problem in a least square sense. Careful analysis of the resolution capability of the used data set has revealed the better-resolved features for interpretation. The resulting 3D model shows a high-velocity layer extending from the northeastern side of the model, where it lays about 30 km deep, to the southwestern part where it shallows to 15 km. The shallow part of this high-velocity body is located near the original area of the opening of the Ligurian Sea that took place between the Oligocene and early Miocene. Its velocity is comparable with that of an oceanic Moho (around 7.8 km/s). A lens-shaped high-velocity body, about 25 km long, located at a depth of 8±15 km, is interpreted as a series of ophiolitic bodies intruded into the upper crust. q 2001 Elsevier Science B.V. All rights reserved. Keywords: Ligurian Sea; tomography; velocity perturbation; Moho
1. Introduction The tectonic features and geodynamics of the Ligurian Sea have been the focus of many studies using different techniques (e.g. Azuende et al., 1973; Burrus, 1984; ReÂhault et al., 1984; Augliera et al., 1994; Makris et al., 1999). In the following, we brie¯y * Corresponding author. Tel.: 139-10-353-8086; fax: 139-10353-8081. E-mail address: [email protected] (S. Solarino).
summarize the principal results to introduce the topic of this paper and to document the need for a revision of the seismic catalogues obtained from the permanent station network in the region (Fig. 1). The Ligurian Sea originated in the counterclockwise rotation, of around 308, of the Corsican± Sardinian block relative to the European plate, due to the convergence of the European and African plates. The tectonic characteristics of this basin are a very shallow Moho, with a depth of only about 20 km (Giese and Buness, 1992; Ginzburg et al.,
0040-1951/01/$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0040-195 1(01)00106-8
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Fig. 1. Distribution of seismic stations around the Ligurian Sea and of OBS. Not all stations were operating at the same time.
1986), a very steep NW margin incised with canyons, and the presence of a system of shallow normal faults parallel to the coast (Chaumillon et al., 1994). The presence of an oceanic crust is documented by refraction data (Recq et al., 1976), revised by De Voogd et al. (1991), and con®rmed by gravity modeling (Klingele et al., 1992) and magnetic anomalies interpretation (Wonik et al., 1992). The tectonic features would seem to con®rm the tensional origin of the basin, but recent seismological evidence has led to a converse opinion: the two major recent earthquakes in the Ligurian Sea (M 6:0; 19 July 1963 earthquakes) show compressive focal mechanisms and the recent computation of the stress ®eld shows the principal horizontal stress vector with a direction NW±SE (Eva and Solarino, 1998).
Furthermore, in a recent paper, Bethoux et al. (1992) suggest that the Ligurian Sea is actually closing and a compression, that would justify the occurrence of the above-mentioned compressional events, is being reactivated. The historically and instrumentally determined seismicity of the Ligurian Sea (Fig. 2) is principally concentrated in the western part and localized at the foot of the continental margin. The seismic activity is mostly con®ned to events of magnitude up to 5.0; few events reach larger magnitudes (M 6:0; 19 July 1963 earthquakes and estimated M 6:0±6:2; 23 February 1887 (Capponi et al., 1980)). Seismic activity is also present offshore but it is neither consistent nor continuous. In particular, there is a lack of seismicity that seems to be almost coincident with a high
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Fig. 2. Seismicity (Period 1981±1998) of the western Alpine chain. The framed area is the object of this study. Solid lines represent the surface heat ¯ux as derived by Pasquale et al. (1996).
heat-¯ux zone outlined by Pasquale et al. (1994, 1996). In this context, it is necessary to better investigate the seismicity and the seismotectonics of this part of the Ligurian Sea. Bethoux et al. (1992) revised seismicity according to one dimensional velocity propagation model. As a main conclusion, they underlined the complexity of the Moho geometry, con®rmed also by Pasquale et al. (1996). This paper is mainly
concerned with a detailed tomographic reconstruction of the area to better de®ne the Moho geometry. The paper is organized as follows: a description of the source data; the selection of high-quality subsets, computation and validation of a 1D reference velocity propagation model; use of the 1D model as initial reference model for the 3D tomographic imaging of the crust using local earthquake data; revision of earthquake locations.
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2. Earthquakes catalogue Our ®rst goal was to collect, reformat and clean a large data set for the Ligurian Sea, consisting of all data available for the period 1982±1999. Several seismic networks [Renass (Strasbourg), Sismalp (Grenoble), LDG (Paris), SSS (ZuÈrich), ING (Rome), RSNI (Regional Seimic Network of northwestern Italy, Genova)] have been operating since the beginning of the 80s in an area encompassing the western Alps and the Ligurian Sea. The long recording period of these permanent networks and several temporary seismic station networks provided a comparatively large data set for an area of moderate seismic activity if all the data were merged. Exchange of data is a real necessity for those seismic events that, occurring between the networks, cannot be precisely located by any single network. For a long time, data have routinely been exchanged more generally to provide the best possible station coverage for locations. It is worth noting that the geographical distribution of the stations in the area under study is very uneven, especially in the southern part. Fig. 1 shows stations that are currently in operation or were operating temporarily in the area and were made available for this work. The map clearly shows the limits and constraints that such a station distribution imposes on location quality. From a theoretical point of view, merging travel time data is a simple and straightforward procedure. In practice, however, different ways of recording and treating the data may result in several sources of potential errors if the merging does not include a careful revision of all available (and sometimes unavailable) information. Bearing this in mind, we reviewed all data included in the catalogue to ensure the reliability of the merging process and to have as a complete catalogue as we could. Picking errors, wrong phase recognition and mistaken station coordinates can strongly bias the quality of locations and the whole catalogue. The revision process was conducted following the procedure described in Solarino et al. (1997) and consisted of several steps, including: ² check and revision of all stations names and coordinates; ² revision of merged arrival times;
² recognition and erasure of doubled and unreliable readings. The resulting data set was relocated with Hypoellipse program (Lahr, 1979) using different velocity propagation models for different groups of stations, in order to reduce the effects of strong lateral heterogeneities. Fig. 2 shows the distribution of seismicity for the whole data set, while the framed area includes the seismicity (more than 10 000 events) which was used as a basis for this study. 3. Computation of 1D reference model Several papers (among others, Kissling et al., 1994, 1995) underline the importance of a 1D reference model for both location and tomographic purposes. Even if we were dubious that a single 1D velocity propagation model, that is only a crude approximation of the real earth, could adequately represent the structural complexity of the Ligurian Sea for modeling purposes, we performed the computation of such a model as it is necessary to obtain a reliable basis for the computation of the 3D propagation model. The calculation of the propagation model is the result of a combined, simultaneous inversion of a large number of selected high-quality events for the 1D velocity model parameters and the hypocenter locations. Different models with about the same residual variance and location precision can usually be obtained from the same data set. The model that coincides best with the surface geology and a priori information on the near-surface structure has to be chosen as the ®nal model, and will be called `minimum' (Kissling, 1988) to indicate that it provides minimum average (RMS) values for all earthquakes used in the inversion. The calculation of a minimum 1D model in conjunction with the checking and selection of the earthquake data is a tedious procedure that can be speeded-up if a great number of starting constrains are applied. This means, for example, that use of a propagation model obtained by other means (refraction seismic pro®les) or for other purposes (a 1D model for a larger or neighboring area) as input could dramatically reduce computation time. In fact in these cases all that needs to be done is to `adapt' the existing model to the new geometry and station
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Fig. 3. Distribution of events and stations for KS approach (top panel) and for EGT approach (bottom panel). The area for EGT approach has a corner cut to avoid Alpine seismicity being too in¯uential (see text).
setting. We decided to use VELEST program (Ellsworth, 1977; Kissling, 1988, 1995) to compute the 1D model because of the high-¯exibility offered by the software. To reduce computation time, considering that a 1D
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model is only the ®rst step in the inversion procedure, we approached the problem using two different models (and data sets) already partly tuned to the area, one based entirely on refraction seismology (EGT in the following, Buness et al., 1989) and one obtained by inversion of local earthquake data (Kissling et al., 1995, KS in the following). Neither of the models is currently used for routine locations at RSNI; location parameters are, in fact, obtained with Hypo techniques (Lahr, 1979) using several velocity models for different groups of seismic stations. This should take into account the various domains (Alps, Ligurian Sea, Apennines) and have `alpine', `apenninic' and `oceanic' models for local seismicity, inside the respective areas. As it is not feasible to merge these models to obtain a unique 1D propagation model, an `averaged' velocity model must be computed. Identical and restrictive criteria have been adopted to select two data sets to be used for computation: We established at least 6 P readings as selecting threshold and a maximum azimuthal gap of 1808. To avoid an overly strong Alpine seismicity in¯uence and to better focus on in-homogeneities and tectonic features of the Ligurian Sea only, additional selection criteria were introduced. In one case (the KS data set) the selection was performed on a squared area, ®ltering the data through a grid-like selection in which only a dozen events were selected per grid square. In the other case (the EGT data set), the northwestern edge of the area was cut off to partially exclude the Alpine seismicity. Fig. 3 reports the adopted geometry and the selected seismicity for KS (top panel) and EGT (bottom panel). Finally, the in¯uence of indirect rays was avoided by selecting only those readings that could be considered ®rst arriving diffracted P phases (recorded less than 100 km from the epicenter) for each event. This is not necessary for the computation of the 1D model with VELEST, but it is necessary to ful®ll the requirements of the approximate ray tracing (ART) used by the 3D inversion code SIMULPS (described in Section 4). The ®nal result of such a comprehensive selection is two different data sets with some common events, one made of 1135 events (KS), the other of 1365 (EGT) recorded by several stations inside and outside the respective areas.
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Fig. 4. Top panels: left, ®nal station corrections for model resulting from 1D computation using KS; right, 1D P velocity initial (solid line) and ®nal (broken line) propagation model. Bottom panel: as above starting from EGT model.
Fig. 4 shows the results of the two computations: initial and ®nal 1D P models are reported with the corresponding station delays. To better understand the performance of the newly computed 1D models, they were used for the relocation of a third, independent data set (cross-validation technique) made of medium-high quality events partly coinciding with those used for the model calculation (Fig. 5). Fig. 6 reports a comparison of the focal parameters and RMS
of the two resulting locations for this data set, applying VELEST in a single event mode (solution of the lonely location problem). Hypocentral and depth differences are of a low order (at most about 0.068, 7 km if latitude is taken into account); the only systematic trend evident is for origin time due to a greater average velocity for shallow layers in the KS model. Since the RMS values have been computed using the same data set and method, the differences
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Fig. 5. Main characteristics of the data set used for estimating quality of propagation model (cross validation technique, see text). Epicentral positions, number of P phases (top), depth distribution and gap (bottom).
in their distributions (bottom panels of Fig. 6, KS left, EGT right) can be attributed only to the capability of the 1D model to ®t the travel time observations. In this sense, the RMS distribution is a helpful tool in choosing a 1D model. Due to the great variability of the Moho under the Ligurian Sea, and especially under the transition zone between the sea and the land, we did not expect a new 1D model to be really able to account for the Moho topography. Therefore, we then proceeded to the next step, using the KS 1D model, to obtain better constraints (locations) from the 3D velocity propagation model. 4. Local tomography and 3D modeling For the 3D inversion, we used the SIMULPS code originally written by Thurber (1983), which performs the inversion using a damped least squares approach and implements a grid-parameterization of the
velocity model, where velocity values are de®ned at grid-nodes and are linearly interpolated between nodes. To solve the forward problem, approximate 3D ray tracing and pseudo bending were applied. Hypocenter locations were updated within the new velocity model at each iteration step. Since the 1D model resulting from the inversion of the KS model turned out to be best, it was used as initial reference model for the tomography, after proper re-parameterization accounting for the main difference between the two inversion schemes. In fact, the output from VELEST is a layered model with constant layer velocities, while the input 1D model for SIMULPS is a gradient model. The choice of suitable damping was made by constructing a trade-off curve (see Fig. 7) and selecting the value that most reduced the data variance without causing a large increase in the solution variance (Eberhart-Philips, 1986). The trade-off curve was computed using a large range of damping values, performing oneiteration inversions. Because of the shape of the curve,
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Fig. 6. Comparison between location parameters adopting the two 1D models (KS minus EGT). (A) latitude, (B) longitude, (C) origin time, (D) depth, (E) and (F) RMS for KS and EGT, respectively.
it was evident that there was a large range of values (300±600) that responded to the goal of reducing data variance without increasing solution variance. Since we did not want to overdamp too much, a value of 300 was chosen (see arrow in Fig. 7). The ®nal 3D model was obtained after seven iterations. To test the improvement introduced by the newly computed 3D model, the independent data set was used for relocation and comparison. The most
important error-estimates (ERH, ERZ, RMS) of the ®nal locations were compared to those obtained using the 1D model and displayed in a comprehensive set of charts (Fig. 8). The use of an appropriate 3D model increased the quality of the determination of focal parameters, consistently but not very much, since the improvement was computed with respect to locations that already represented the `best' obtainable using a 1D model.
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Fig. 7. Trade-off curve for selecting the optimum damping for the P data set. A wide range of values ensures reduction of data variance without causing a large increase in the solution variance. The shape of the curve is probably due to the non-homogeneity of the data set.
An aerial view of the resulting 3D structure is reported in Fig. 9, where we plotted percentage velocities with respect to the average velocity computed over sample nodes for each layer at depths of 3, 10, 15, 20, 25 and 30 km. Only those areas, in which the sampling was greater than 10, have been reported. As we are much more interested in deeper structures, and considering that shallow layers are much more biased by uneven ray distribution (due
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to station distribution), special attention was devoted to inversion results for deep layers. A careful analysis of reliability must be conducted before any interpretation of tomographic results can be made. In fact, any tomographic image is only as good as its resolution estimates. When assessing the quality of the tomographic inversion, one must determine how well each node was illuminated (number of rays hitting the node) and how well it was resolved. The very rough estimate of illumination of the model contained in the hit count matrix (reporting the number of rays that contribute to the solution of that node) is supported by the derivative weight sum (DWS, Fig. 10), a more sensitive measurement of the spatial sampling of the model space. It quanti®es the relative ray density in the volume of in¯uence of a model node, weighing the importance of each ray segment by its distance from the model node (Haslinger et al., 1999). The best and quickest estimate of the quality of the solution is provided by the resolution diagonal element (RDE, Fig. 11). It is a diagnostic tool which shows the degree of independence of one model
Fig. 8. Comparison of 1D (top) and 3D (bottom) locations for KS model. Use of a 3D model slightly increases the quality of (already good) locations.
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Fig. 9. Aerial views of tomographic results. Grey is for unsampled areas. The solid white contours depict reliably resolved areas. Velocity percentages are computed with respect to the average velocity over all sampled nodes. FT: Furia-Taggia Canyon; Var: Var Canyon; IB: Ivrea Body.
parameter in the solution by showing the diagonal element of the full solution matrix; it ranges from 0.0 to 1.0 and the larger it is, the more independent the solution is.
A consistent problem is how to use these diagnostic tools once they are computed. One should ®x reliability limits that tend to have good sampling, high values of RDE and DWS, for a certain area. Haslinger (1998)
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Fig. 10. Derivative weight sum values for the planes shown in Fig. 9.
suggested that not even synthetic tests conducted on a ®xed model geometry and data set could quantitatively determine the choice of upper and lower reliability bounds. The choice is therefore somewhat arbitrary. Because of geographical constraints we
could not be very conservative in this work, and we decided to consider all areas in which RDE was greater than 0.1 and DWS greater than 100 as fairly resolved. A white contour line circumscribes the areas where the quality requirements are ful®lled, and
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Fig. 11. Resolution diagonal element values for the planes shown in Fig. 9.
everything lying inside is taken into account in the description, interpretation and discussion of the results. Looking at the aerial views, all layers show strong lateral velocity variations, reaching maximum values of more than 15%: nevertheless, because of the
spot-like images (high frequency spatial anomalies) and the complexity of the tectonic setting of the area investigated, it would appear to be dif®cult to obtain a unique and complete interpretation of the tomographic results. We could distinguish between two different anomaly
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Fig. 12. Positions of the NNW±SSE (left) and NE±SW (right) tomographic cross sections shown in Fig. 13.
patterns in the shallow (3, 10 km) and deeper layers (15, 20, 25, 30 km). Velocity anomalies in the shallow layers are mainly related to the outcropping geological structures. The northern part of the model is characterized by two high-velocity anomalies, both extending to a depth of 20 km, one located east of the Argentera Massif the other located in the NE corner of the model. The ®rst anomaly could be related to the southern part of the Ivrea body, as proposed by previous tomographic studies (Solarino et al., 1997). This complex of high-density, high-velocity and high-susceptibily rocks is supposed to extend to a depth of more than 30 km in the northern part (close to the Ivrea-Verbano area, where it outcrops) and to ¯oat in the southern part. Our results con®rm such behavior, at least for the part included in our tomographic image. In fact, we do not see a high-velocity body below 20 km of depth. The other velocity perturbation can be related to the magnetic anomalies investigated in previous studies (Froidevaux and Guillaume, 1979; Bozzo et al., 1986; Bozzo et al., 1992) but never con®rmed by tomographic results. An extended high-velocity anomaly located in the SW corner of the model is the main feature of the deeper layers, in contrast with a low-velocity anomaly at all depths in the northeastern sector of the Ligurian Sea. 5. Discussion and conclusions A few tomographic cross-sections, reporting
absolute velocity values, are displayed in Figs. 12 and 13. In particular, Fig. 12 shows three crosssections oriented NW±SE, while Fig. 13 displays cross-sections parallel to the coast. In the following, we make use of these to try to comment on the tectonic setting of the complex Ligurian Sea area. The high-velocity body, strongly related to a concentrated and consistent seismic activity, already demonstrated by the analysis of tomographic maps (see previous paragraph) is also clearly evident in the AB cross-section (Fig. 12). It appears as a 25 km long, lens-shaped body located at a depth of 8±13 km. On the basis of gravimetric and magnetic observations, respectively, Froidevaux and Guillaume (1979) and Bozzo et al. (1992) interpreted this anomaly as the existence of ophiolitic bodies seated deep in the upper crust. A low velocity zone is also clearly evident in the sections perpendicular and parallel to the coast: in the CD cross-section low velocities are displayed to a depth of 3 km, extending from the coast to the foot of the continental margin. Even if the reliability of the tomographic image of this low velocity area is questionable, this anomaly can be interpreted as evidence of the very thick layers of sediments, at least for the ®rst kilometers, related to the presence of two deep, active canyons oriented perpendicular to the coast (canyons of Furia-Taggia and Var) (Chaumillon et al., 1994). The deeper, low velocity, layer clearly evident in the northeastern part of the tomographic model (see cross-sections GH and IL), indicates a
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Fig. 13. Tomographic cross sections in absolute velocity. AB, CD and EF are almost perpendicular to the coast while GH and IL are parallel to it (see Fig. 12).
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strong crustal thickening up to 25 km deep that could be related to the triple junction between the Alpine, Apenninic and Ligurian structures. In particular, a crustal indenter could be hypothesized in an area where the principal tectonic elements have their rotation point and the two principal plates are pushing one each other. Assuming a velocity of 7.8 km/s for the crust±mantle boundary (Bethoux et al., 1986), in cross-sections GH and EF, the depth of the Moho appears strongly variable. In the northern and eastern part of the tomographic model, the depth of the Moho is about 30 km, deepening to 36 km, in the central part of the CD cross-section, and shallowing to 15 km in the southwestern part of the imaged model (crosssection GH). Even if these results roughly con®rm the position of the ocean boundaries predicted by several authors, a number of questions still remain. In particular, the studies of the opening of the Ligurian Sea, on the basis of heat ¯ow (Pasquale et al., 1996), gravimetric (Morelli et al., 1977; Klingele et al., 1992), magnetic (Galdeano and Rossignol, 1977; Wonik et al., 1992) and wide-angle seismic pro®le interpretation (Makris et al., 1999), have led to the hypothesis of the presence of an oceanic crust in the bathyal plain of the Ligurian basin. Actually, because of the scarce tomographic resolution in the central part of the Ligurian Sea (see thick white line in Fig. 9), this hypothesis cannot be supported by the reconstructed images. On the other hand, in crosssections GH and IL (north of the hypothesized oceanic Moho of other authors), the transition between the oceanic realm (southwestern side of the crosssections) and the continental part of the Ligurian Sea is also well de®ned by the behavior of the layer, with a velocity of more then 7.0 km/s. P-wave velocities ranging from 7.0 to 7.7 km/s are typical of the 3B layer of an oceanic crust and indicate a cumulatericher gabbro layer at the base of the crust. Moreover, the pattern of the re-located seismicity (see top panel of Figs. 12 and 13) shows that seismic activity is mainly concentrated on the borders of the high velocity zone, while it disappears within it. Acknowledgements We wish to thank Edi Kissling and an anonymous
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reviewer for providing us not only with a constructive reviews but also with useful ideas for further investigations and additional references. This paper would not have been possible without the daily efforts of all the staff of the Genova seismic network. We thank them all. This work was supported by the DEVINE Interreg II Project. References Augliera, P., Bethoux, N., Deverchere, J., Eva, C., 1994. The Ligurian Sea: new seismotectonic evidence. Boll Geof. Teor. Appl., XXXVI, 363±380. Azuende, J.M., Bonnin, J., Olivet, J.L., 1973. The origin of the western Mediterranean basin. Geol. Soc. London 129, 607±620. Bethoux, N., Petit, F., Rehault, J.P., Massinon, B., Montagner, J.P., 1986. Several location methods for underwater shots in the Gulf of Genoa (Western Mediterranean): structural implications. Tectonophysics 128, 357±379. Bethoux, N., FreÂchet, J., Guyoton, F., Thouvenot, F., Cattaneo, M., Eva, C., Nicolas, M., Granet, M., 1992. A closing Ligurian Sea? Pure Appl. Geophys. 139, 179±194. Bozzo, E., Cattaneo, M., Giglia, G., Pegoraro, G., 1986. Deep magnetic structures in the Piedmontese Tertiary Basin (Ligurian±Piedmontese area, Northern Italy). Boll. Geof. Teor. Appl., XXVIII 109, 49±63. Bozzo, E., Campi, S., Capponi, G., Giglia, G., 1992. The suture between the Alps and Apennines in the Ligurian sector based on geological and geomagnetic data. Tectonophysics 206, 159±169. Buness, H., Giese, O., Hirn, A., Scarascia, S., 1989. Crustal structure derived from siesmic refraction between the Southern Alps and the Ligurian Sea. In: Freemen, R., Mueller, St. (Eds.), Proc. 6th Workshop on the European Geotraverse (EGT) Project, pp. 165±167. Burrus, J., 1984. Contribution to a geodynamic synthesis of the Provencal basin (north western Mediterranean). Mar. Geol. 55, 247±270. Capponi, G., Eva, C., Merlanti, F., 1980. Il terremoto del 23 Febbraio 1887 in Liguria occidentale. Atti Acad. Ligure Sci. Lett. 37, 1±33. Chaumillon, E., DevercheÁre, J., ReÂhault, J.P., Gueguen, E., 1994. ReÂactivation tectonique et ¯exure continentale Ligure (MeÂditerraneÂe Occidentale). C. R. Acad. Paris 319, 675±682. De Voogd, B., Nicolich, R., Olivet, J.L., Fanucci, F., Burrus, J., Mauffret, A., Pascal, G., Arniani, A., Auzende, J.M., Bernabini, M., Bois, C., Carmignani, L., Fabbri, A., Finetti, I., Galdeano, A., Gorini, C.Y., Labaume, P., Lajat, D., Patriat, P., Pinet, B., Ravat, J., Ricci Lucchi, F., Vernassa, S., 1991. First deep seismic re¯ection transect from the Gulf of Lions to Sardinia (ECORS-CROP pro®le in Western Mediterranean). In: Meissner, R., Brown, L., Durbaume, H.J., Franke, W., Fuchs, K., Seifert, F. (Eds.), Continental Lithosphere: Deep Seismic Re¯ection, Am. Geophys. Union, Geodyn. Ser. 22, 265±274.
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