Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Wu, Hao; Greer, Sarah Y.; O’Malley, Daniel

doi:10.1038/s41598-022-26898-1

Title: Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Journal Article · 12 January 2023 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-022-26898-1 · OSTI ID:1909066

Wu, Hao; Greer, Sarah Y.; O’Malley, Daniel

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 algorithmic 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 algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; SC0019323

OSTI ID:: 1909066

Report Number(s):: LA-UR-22-27628; 718; PII: 26898

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 13; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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