论文标题
将深度学习与完整波形反演耦合
Coupling Deep Learning with Full Waveform Inversion
论文作者
论文摘要
完整的波形反演（FWI）旨在使用从多个传入来源生成的波场数据重建波方程中未知的物理系数。在这项工作中，我们提出了一种离线在线计算策略，用于将基于最小二乘基于最小二乘的经典计算倒置与现代深度学习的方法相连，以实现仅使用其中一个组件而无法实现的优势。简而言之，我们制定了一个离线学习策略，以构建与逆操作员的强大近似值，并利用它通过新数据集为在线反转设计新的目标函数。我们通过数值模拟证明，我们的耦合策略通过对中等计算资源的可靠离线培训提高了FWI的计算效率（就培训数据集的规模和所需的计算成本而言）。
Full waveform inversion (FWI) aims at reconstructing unknown physical coefficients in wave equations using the wave field data generated from multiple incoming sources. In this work, we propose an offline-online computational strategy for coupling classical least-squares based computational inversion with modern deep learning based approaches for FWI to achieve advantages that can not be achieved with only one of the components. In a nutshell, we develop an offline learning strategy to construct a robust approximation to the inverse operator and utilize it to design a new objective function for the online inversion with new datasets. We demonstrate through numerical simulations that our coupling strategy improves the computational efficiency of FWI with reliable offline training on moderate computational resources (in terms of both the size of the training dataset and the computational cost needed).
