论文标题
自动分化的地震反转的一般方法
A General Approach to Seismic Inversion with Automatic Differentiation
论文作者
论文摘要
从地震数据中成像地球结构或地震来源涉及最大程度地减少目标失配函数，并且通常通过基于梯度的优化来解决。已经开发了伴随状态方法来有效地计算梯度。但是，其实施可能是耗时且困难的。我们开发了一个一般的地震反转框架，以使用反向模式自动分化来计算梯度。核心思想是，伴随状态方法和反向模式自动分化在数学上是等效的。数值PDE模拟与深度学习之间的映射使我们能够基于深度学习框架构建地震反向建模库Adseismic，该框架支持高性能反向模式自动差异化的CPU和GPU。我们证明了与速度模型估计，破裂成像，地震位置和源时间函数检索有关的反问题的表现。 Adseismic有可能在统一框架内解决各种各样的反模型应用。
Imaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has been developed to compute the gradient efficiently; however, its implementation can be time-consuming and difficult. We develop a general seismic inversion framework to calculate gradients using reverse-mode automatic differentiation. The central idea is that adjoint-state methods and reverse-mode automatic differentiation are mathematically equivalent. The mapping between numerical PDE simulation and deep learning allows us to build a seismic inverse modeling library, ADSeismic, based on deep learning frameworks, which supports high performance reverse-mode automatic differentiation on CPUs and GPUs. We demonstrate the performance of ADSeismic on inverse problems related to velocity model estimation, rupture imaging, earthquake location, and source time function retrieval. ADSeismic has the potential to solve a wide variety of inverse modeling applications within a unified framework.
