Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data

This paper presents Latent-Space Inversion (LSI) as a new data-informed inversion and parameterization framework where dimensionality reduction is tailored to flow physics that governs the behavior of subsurface systems. Inverse modeling in hydrogeology and petroleum engineering involves minimizing...

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Veröffentlicht in:	Computational geosciences Jg. 26; H. 1; S. 71 - 99
Hauptverfasser:	Razak, Syamil Mohd, Jiang, Anyue, Jafarpour, Behnam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.02.2022 Springer Nature B.V
Schlagworte:	Calibration Contamination Data integration Deep learning Earth and Environmental Science Earth Sciences Feature extraction Flow mapping Flow simulation Geology Geotechnical Engineering & Applied Earth Sciences Hydrogeology Inverse problems Kalman filters Large scale integration Learning algorithms Learning behaviour Machine learning Mapping Mathematical Modeling and Industrial Mathematics Mathematical models Original Paper Parameterization Parameters Petroleum engineering Physics Redundancy Soil Science & Conservation Training Wavelet transforms Deep learning Subsurface flow Model calibration Parameterization Latent space inversion
ISSN:	1420-0597, 1573-1499
Online-Zugang:	Volltext
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