Slip Tendency Analysis From Sparse Stress and Satellite Data Using Physics‐Guided Deep Neural Networks

Autor: Thomas Poulet, Pouria Behnoudfar
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geophysical Research Letters, Vol 51, Iss 12, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2024GL109524
Popis: Abstract The significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, we propose a novel approach using a physics‐informed neural network that integrates stress orientation and satellite displacement observations in a top‐down multi‐scale framework to estimate two‐dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula.
Databáze: Directory of Open Access Journals
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