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

Hao Wu1, Sarah Y Greer2,3, Daniel O'Malley2

  • 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. wu_hao@lanl.gov.

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Summary
This summary is machine-generated.

This study introduces a physics-embedded generative model for accurate geological property estimation. Algorithmic differentiation enhances this inverse analysis approach, proving reliable for complex subsurface characterization.

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Area of Science:

  • Geophysics
  • Geology
  • Computational Science

Background:

  • Inverse analysis is crucial for determining subsurface geological properties.
  • Traditional methods often face underconstrained problems and performance limitations.

Purpose of the Study:

  • To present a novel physics-embedded generative model for inverse analysis.
  • To improve the accuracy and reliability of subsurface property characterization.

Main Methods:

  • Utilized a physics-embedded generative model integrating physical laws.
  • Employed algorithmic differentiation for efficient inverse analysis.
  • Tested on heterogeneous hydraulic conductivity, hydraulic fracture networks, and seismic P-wave velocity inversion.

Main Results:

  • The physics-embedded approach accurately characterized diverse geological problems.
  • Demonstrated excellent performance in matching observational data, confirming reliability.
  • Showcased consistent and accurate subsurface property estimation.

Conclusions:

  • The proposed method offers a robust solution for inverse analysis in complex geological settings.
  • Algorithmic differentiation facilitates a fast and accessible approach to inverse problems.
  • This technique enhances the understanding of underground geological properties.