Ambient seismic noise tomography of SW Iberia integrating seafloor- and land-based data

Abstract

We used ambient seismic noise recorded by 24 Broadband Ocean Bottom Seismometers (OBS) deployed in the Gulf of Cadiz during the EC funded NEAREST project and seven broadband land stations located in the South of Portugal to image the sedimentary and crustal structure beneath the Eastern Atlantic and SW Iberia. We computed ambient noise cross-correlations to obtain empirical Green's functions (EGFs) between all station pairs using land seismometers and both OBS sensors, seismometers and hydrophones. Despite the great difference in the recording conditions and local crustal structure between the OBSs and land stations, we could compute EGFs, by applying a linear cross-correlation with running absolute mean average time normalization, followed by a time-frequency phase weighted stack. Dispersion analysis was then applied to the EGFs, between 4 and 20s period. The obtained dispersion curves allowed mapping the lateral variation of Rayleigh-wave group velocities, as a function of period. Finally, dispersion curves extracted from each cell of the 2D group velocity maps were inverted, as a function of depth, to obtain the 3D distribution of the shear-wave velocities. The 3-D shear wave velocity model, computed from joint inversion of OBSs and land stations data allowed to estimate the thickness of sediments and crust and the Moho depth. Despite the gap that exists between the OBSs and land station locations, our model displays a good correlation with the known geological structure. The derived sedimentary layer and crustal thicknesses and the obtained Moho depth are locally in agreement with the models proposed by other studies using near vertical, refraction and wide-angle seismic profiling. We conclude that ambient noise tomography could be a valuable tool to image oceanic domains, and also that it is possible to integrate seafloor- and land-based stations to derive a structure model in the transition domain between continent and ocean. © 2017 Elsevier B.V.