Attention and Hybrid Loss Guided Deep Learning for Consecutively Missing Seismic Data Reconstruction

Abstract

Missing trace reconstruction is an essential step in the seismic data processing. Various interpolation methods have been proposed for handling this issue. In recent years, deep learning-based interpolation techniques, especially convolutional neural networks (CNNs), have been widely studied. Typically, these studies target regularly/randomly missing cases, leaving consecutively missing situations not handled properly. In this article, we propose a hybrid loss function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {SSIM}+L_{1}$ </tex-math></inline-formula> , based on structural similarity (SSIM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm, for network training and attention mechanism as a network component that explicitly utilizes global information. We further design a CNN equipped with the hybrid loss and attention mechanism for successively missing trace reconstruction. Experiments on synthetic and field data demonstrate that our network can reconstruct more reasonable results than networks without attention mechanism in large gap situation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {SSIM}+L_{1}$ </tex-math></inline-formula> loss promotes interpolation results. We also discuss the setup of key hyperparameters of the network by a thorough ablation study.