Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning

TL;DR

This paper introduces a physics-informed generative adversarial network framework for seismic full waveform inversion, improving model accuracy and robustness against local minima and noise without requiring labeled data.

Contribution

It presents a novel unsupervised learning approach combining wave equations and GANs for geophysical imaging, eliminating the need for pretraining or labeled datasets.

Findings

Outperforms classical algorithms on synthetic models

Reduces sensitivity to initial models and noise

Sidesteps local-minima issues in FWI

Abstract

Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the simulated and observed seismograms. Unfortunately, conventional FWI with least-squares function suffers from many drawbacks such as the local-minima problem and computation of explicit gradient. It is particularly challenging with the contaminated measurements or poor starting models. Recent works relying on partial differential equations and neural networks show promising performance for two-dimensional FWI. Inspired by the competitive learning of generative adversarial networks, we proposed an unsupervised learning paradigm that integrates wave equation with a discriminate network to accurately estimate the physically consistent models in a distribution sense. Our framework needs no labelled training data nor pretraining of the network, is flexible to achieve multi-parameters inversion with minimal user interaction. The proposed method faithfully recovers the well-known synthetic models that outperforms the classical algorithms. Furthermore, our work paves the way to sidestep the local-minima issue via reducing the sensitivity to initial models and noise.