Abstract

Estimating shallow velocity from land seismic data is challenging due to severe noises, which are largely caused by complexities in the near-surface weathering layers. One such complexity arises from velocity reversals, which imposes challenges for conventional methods to estimate near surface models. We proposed a deep learning framework, trained with field data-driven synthetics and then combined with full-waveform inversion, to estimate the near-surface velocity model with reversals.

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