Physics-embedded inverse analysis with automatic differentiation for the earth's subsurface

TL;DR

This paper introduces a physics-embedded inverse analysis method enhanced with automatic differentiation, improving the accuracy and efficiency of characterizing complex subsurface geological properties from observational data.

Contribution

The paper presents a novel physics-embedded generative model combined with automatic differentiation for improved inverse analysis of geological properties.

Findings

Accurately characterizes diverse geological problems

Demonstrates reliable matching of observational data

Offers a fast and easy approach for complex structures

Abstract

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.