The use of convolutional neural networks to search for fractures in the geological media

DOI10.1134/S1995080223010298zbMath1515.74055OpenAlexW4377003483MaRDI QIDQ6043913

Igor B. Petrov, D. I. Petrov, D. S. Konov, M. V. Muratov

Publication date: 25 May 2023

Published in: Lobachevskii Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s1995080223010298

zbMATH Keywords

inverse problemmachine learningexploration seismologygrid-characteristic methodpartial glued fracture, wave response

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Brittle fracture (74R10) Fluid-solid interactions (including aero- and hydro-elasticity, porosity, etc.) (74F10) Seismology (including tsunami modeling), earthquakes (86A15) Inverse problems for waves in solid mechanics (74J25) Geophysical solid mechanics (74L05) Geological problems (86A60) Numerical and other methods in solid mechanics (74S99)

Cites Work

• On the use of hybrid grid-characteristic schemes for the numerical solution of three-dimensional problems in the dynamics of a deformable solid
• Применение сеточно-характеристического метода в решении прямых задач сейсморазведки трещиноватых пластов (обзорная статья)

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