“Missing-As-Complete” (MAC) Strategy and Hybrid Loss Guided Network Training for Seismic Data Reconstruction

Abstract

Missing trace reconstruction is a basic step in seismic data processing workflow. Recently, many deep learning based seismic data reconstruction methods have been proposed. However, lack of label data can impair the performance for practical applications due to domain gaps on seismic data prior. In this research, we propose a “Missing-As-Complete” (MAC) strategy for training networks to solve the problem of missing labels in practical situation. Specially, the missing seismic data is directly taken as the “complete” target. While the input is seismic data consisted of missing trace and a second missing trace mask. A hybrid loss function FFL+SSIM +L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> based on the focal frequency loss (FFL), structural similarity (SSIM) and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm is used to further improve the reconstruction performance. Experiments on synthetic data demonstrate that the network can reconstruct reasonable results by the proposed method.