Deep-learning-based low-frequency reconstruction for full-waveform inversion

Abstract

Low-frequencies (LF) are vital for full-waveform inversion (FWI) to retrieve accurate long-scale features and then reliable subsurface properties from seismic data, which are missing in the acquired seismic data due to limitations in acquisition steps. Low-frequency reconstruction (LFR) is imperative. The recently developed deep learning (DL) based LFR methods are based on 1D/2D convolutional neural networks (CNNs), which cannot take full advantage of the information in the 3D seismic data. We develop a DL-based approach for LFR in which the high-frequencies (HF) (e.g., ≥ 10 Hz) are transformed into the LF counterparts (i.e., < 10 Hz) by training an end-to-end 3D CNN. Benchmarking experiments on LFR for FWI indicate the FWI result of the DL-predicted LF nearly resembles that of the true LF, and the DL-predicted LF are accurate enough to solve the cycle-skipping problem of FWI.