Abstract
Seismic surveys in the vast territory of Eastern Siberia are carried out in seismic and geological conditions of varying complexity. Obtaining a high-quality dynamic seismic image for the area of work is a priority task in the conditions of contrasting heterogeneities of the near-surface. For this, it is necessary to restore an effective depth-velocity model that provides compensation for velocity anomalies and calculates static corrections. However, for the most complex near-surface structure, for example, the presence of trap intrusions and tuffaceous formations, the information content of the velocity models of the near-surface area obtained on the basis of tomographic refinement turns out to be insufficient, and a search for another solution is required. The paper considers an approach based on the full waveform inversion (FWI). As the authors showed earlier, the use of multiples associated with the free surface reduces the resolution of this approach but increases the stability of the solution in the presence of uncorrelated noise. Therefore, at the first stage of FWI, the entire wavefield is used, including free surface-related multiples. The data after the suppression of multiples is then used. The obtained results demonstrate the ability of the FWI to restore complex geological structures of the near-surface area, even in the presence of high-velocity anomalies (trap intrusions).
Supported by RSF grant 21-71-20002.
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Acknowledgements
K. Gadylshin is supported by RSF grant 21-71-20002, he performed all numerical FWI experiments. The numerical results of the work were obtained using computational resources of Peter the Great Saint-Petersburg Polytechnic University Supercomputing Center (scc.spbstu.ru).
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Gadylshin, K., Tcheverda, V., Tverdokhlebov, D. (2021). Reconstruction of the Near-Surface Model in Complex Seismogeological Conditions of Eastern Siberia Utilizing Full Waveform Inversion with Varying Topography. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12949. Springer, Cham. https://doi.org/10.1007/978-3-030-86653-2_37
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