论文标题
物理构成的反向分析，并自动分化地球地下
Physics-embedded inverse analysis with automatic differentiation for the earth's subsurface
论文作者
论文摘要
通过将观测数据与模拟器匹配，已经利用了反向分析来理解未知的地下地质特性。为了克服反问题的不足性质并实现良好的性能，用嵌入式物理学和一种称为自动分化的技术提出了一种方法。我们使用物理装置的生成模型，该模型将统计学上的简单参数作为输入，并输出地下属性（例如，渗透性或P波速度），将地下属性的物理知识嵌入反向分析中并改善其性能。我们测试了这种方法在四个地质问题上的应用：两个异质的水力传导率场，一个液压断裂网络和p波速度的地震反转。这种物理学的反向分析方法始终如一地准确地表征了这些地质问题。此外，与观测数据相匹配的出色性能证明了该方法的可靠性。此外，在处理复杂的地质结构时，自动差异化的应用使它成为一种简单而快速的反向分析方法。
Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as automatic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of automatic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.
