Deep Learning with Adaptive Attention for Seismic Velocity Inversion

Abstract

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient and accurate nonlinear algorithm, deep learning (DL) has been used to estimate velocity models. However, conventional DL is insufficient to characterize detailed structures and retrieve complex velocity models. To address the aforementioned problems, we propose a hybrid network (AG-ResUnet) involving fully convolutional layers, attention mechanism, and residual unit to estimate velocity models from common source point (CSP) gathers. Specifically, the attention mechanism extracts the boundary information, which serves as a structural constraint in network training. We introduce the structural similarity index (SSIM) to the loss function, which minimizes the misfit between predicted velocity and ground truth. Compared with FWI and other networks, AG-ResUnet is more effective and efficient. Experiments on transfer learning and noisy data inversion demonstrate that AG-ResUnet makes a generalized and robust velocity prediction with rich structural details. The synthetic examples demonstrate that our method can improve seismic velocity inversion, contributing to guiding the imaging of geological structures.