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

• URL: http://arxiv.org/abs/2109.11452v1
• Date: Thu, 23 Sep 2021 15:54:40 GMT
• Title: Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning
• Authors: Fangshu Yang, Jianwei Ma
• Abstract summary: Full waveform inversion produces high-resolution subsurface models. FWI with least-squares function suffers from many drawbacks such as the local-minima problem. Recent works relying on partial differential equations and neural networks show promising performance for two-dimensional FWI. We propose an unsupervised learning paradigm that integrates wave equation with a discriminate network to accurately estimate the physically consistent models.
• Score: 2.12121796606941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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