Surface-consistent Seismic Data Amplitude Correction Via Learning from Synthetic Models Based On Waveform Modeling

Abstract

Nowadays waveform modeling-based seismic imaging schemes, such as RTM and FWI, are widely used for the subsurface imaging. The use of waveform modeling-based imaging schemes for land seismic data is not always a
straightforward process, and their pre-processing plays key role in the final result. One of the pre-processing steps is surface-consistent amplitude correction to deal with source/receiver response sensitivity and adjust the amplitude of field data to make it suitable for the used imaging scheme. In this paper, we present a novel surface amplitude correction framework based on utilizing the amplitude spectrum of the modeled data produced from some synthetic models in order to tackle these problem. A synthetic and a field data example are shown to discuss the effectiveness of the proposed approach.