论文标题
ML不满：学习使用机器学习的全波倒置的强大合适函数
ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning
论文作者
论文摘要
用于全波形反演（FWI）的大多数可用的高级不合适函数都是手工制作的，这些不合适函数的性能依赖于数据。因此，我们建议根据机器学习学习FWI的不合适功能，标题为ML-Simpit。受匹配滤波器失误的最佳传输的启发，我们以类似于比较两个分布的平均值和方差的形式设计了一个神经网络（NN）架构。为了确保所产生的学习不合适是一个指标，我们可以在元损失函数中与其输入和铰链损失正规化项相适应的对称性，以满足“三角形不平等”规则。在元学习的框架中，我们通过运行FWI来训练网络以反转随机生成的速度模型，并通过最大程度地减少元损坏来更新NN的参数，该元数据被定义为真实模型和倒置模型之间的累积差异。我们首先说明了ML不满的基本原理，用于学习用于旅行时间转移信号的凸效功能。此外，我们在2D水平分层模型上训练NN，并通过将其应用于众所周知的Marmousi模型来证明学习ML不满的有效性和鲁棒性。
Most of the available advanced misfit functions for full waveform inversion (FWI) are hand-crafted, and the performance of those misfit functions is data-dependent. Thus, we propose to learn a misfit function for FWI, entitled ML-misfit, based on machine learning. Inspired by the optimal transport of the matching filter misfit, we design a neural network (NN) architecture for the misfit function in a form similar to comparing the mean and variance for two distributions. To guarantee the resulting learned misfit is a metric, we accommodate the symmetry of the misfit with respect to its input and a Hinge loss regularization term in a meta-loss function to satisfy the "triangle inequality" rule. In the framework of meta-learning, we train the network by running FWI to invert for randomly generated velocity models and update the parameters of the NN by minimizing the meta-loss, which is defined as accumulated difference between the true and inverted models. We first illustrate the basic principle of the ML-misfit for learning a convex misfit function for travel-time shifted signals. Further, we train the NN on 2D horizontally layered models, and we demonstrate the effectiveness and robustness of the learned ML-misfit by applying it to the well-known Marmousi model.
