Surface wave tomography using 3D active-source seismic data

Surface wave tomography (SWT) is a powerful and well-established technique to retrieve 3D shear-wave (S-wave) velocity models at the regional scale from earthquakes and seismic noise measurements. We have applied SWT to 3D active-source data, in which higher modes and heterogeneous spatial sampling make phase extraction challenging. First, synthetic traveltimes calculated on a dense, regular-spaced station array are used to test the performance of three different tomography algorithms (linearized inversion. Markov chain Monte Carlo [MCMC], and eikonal tomography). The tests suggest that the lowest misfit to the input model is achieved with the MCMC algorithm, at the cost of a much longer computational time. Then, real phases were extracted from a 3D exploration data set at different frequencies. This operation included an automated procedure to isolate the fundamental mode from higher order modes, phase unwrapping in two dimensions, and the estimation of the zero-offset phase. These phases are used to compute traveltimes between each source-receiver couple, which are input into the previously tested tomography algorithms. The resulting phase-velocity maps show good correspondence. highlighting the same geologic structures I'm all three methods. Finally, individual dispersion curves obtained by the superposition of phase-velocity maps at different frequencies are depth inverted to retrieve a 3D S-wave velocity model.

Automated summary

Surface wave tomography is a powerful technique used to retrieve 3D shear-wave velocity models at a regional scale. By applying this technique to 3D active-source data and testing different tomography algorithms, it was found that the Markov chain Monte Carlo (MCMC) algorithm achieved the lowest misfit to the input model. The resulting phase-velocity maps showed good correspondence and highlighted the same geologic structures, leading to the retrieval of a 3D S-wave velocity model through depth inversion of individual dispersion curves.