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.

翻译：全波形反演（FWI）是一种强大的地球物理成像技术，通过求解非凸优化问题来推断高分辨率的地下物理参数。然而，由于观测条件的限制（例如有限的炮点或接收器）以及随机噪声的存在，传统反演方法面临着局部极小值问题等诸多挑战。近年来，大量研究表明，将深度神经网络与偏微分方程相结合以求解全波形反演问题，已展现出优越的性能。在本工作中，受神经网络表达能力的启发，我们提出了一种无监督学习方法，旨在精确重构地下物理速度参数。该方法基于贝叶斯推断中的重新参数化技术，通过具有随机权重的深度神经网络实现。值得注意的是，我们提出的方法不依赖于标注训练数据集，因此具有极高的通用性，能够适应不同的地下模型。大量实验表明，所提方法的性能显著优于现有的传统反演方法。