2023/11/08 by Xiong-Bin Yan, Keke Wu, Yan, Xiong-Bin +5
Earth and Planetary Sciences · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Geophysical Methods and Applications #Machine Learning (cs.LG) #Numerical Analysis (math.NA) #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis
paper · pdf · doi:10.48550/arxiv.2311.04531
openalex publication_date 2023/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-resolution subsurface physical parameters by solving a non-convex optimization problem. However, due to limitations in observation, e.g., limited shots or receivers, and random noise, conventional inversion methods are confronted with numerous challenges, such as the local-minimum problem. In recent years, a substantial body of work has demonstrated that the integration of deep neural networks and partial differential equations for solving full-waveform inversion problems has shown promising performance. In this work, drawing inspiration from the expressive capacity of neural networks, we provide an unsupervised learning approach aimed at accurately reconstructing subsurface physical velocity parameters. This method is founded on a re-parametrization technique for Bayesian inference, achieved through a deep neural network with random weights. Notably, our proposed approach does not hinge upon the requirement of the labeled training dataset, rendering it exceedingly versatile and adaptable to diverse subsurface models. Extensive experiments show that the proposed approach performs noticeably better than existing conventional inversion methods.