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This article is cited in 4 scientific papers (total in 4 papers)
INFORMATICS
Solution of the fracture detection problem by machine learning methods
M. V. Muratov, V. A. Biryukov, I. B. Petrov Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow Region, Russian Federation
Abstract:
Inverse problems of fracture exploration seismology are solved using machine learning methods. A single fracture of fixed size and subvertical orientation is considered in the two-dimensional case. The spatial position and the inclination angle of the fracture are determined using a neural network. The training set consists of solutions of direct problems produced by the grid-characteristic method on regular rectangular meshes in the form of synthetic seismograms obtained by measuring the vertical velocity on the surface of the medium.
Keywords:
mathematical modeling, grid-characteristic method, machine learning, neural networks, inverse exploration seismology problem, fracture.
Received: 28.06.2019 Revised: 05.12.2019 Accepted: 24.01.2020
Citation:
M. V. Muratov, V. A. Biryukov, I. B. Petrov, “Solution of the fracture detection problem by machine learning methods”, Dokl. RAN. Math. Inf. Proc. Upr., 491 (2020), 107–110; Dokl. Math., 101:2 (2020), 169–171
Linking options:
https://www.mathnet.ru/eng/danma60 https://www.mathnet.ru/eng/danma/v491/p107
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