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

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 know...

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Bibliographic Details
Published in:Scientific reports Vol. 13; no. 1; pp. 718 - 14
Main Authors: Wu, Hao, Greer, Sarah Y., O’Malley, Daniel
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.01.2023
Nature Publishing Group
Nature Portfolio
Subjects:
704/2151/241
704/2151/508
Algorithms
Geological structures
Geology
Humanities and Social Sciences
multidisciplinary
Permeability
Physics
Science
Science (multidisciplinary)
Velocity
Wave velocity
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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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USDOE
20200575ECR; SC0019323
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-26898-1