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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2020, Volume 491, Pages 107–110
DOI: https://doi.org/10.31857/S2686954320020162
(Mi danma60)
 

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
Full-text PDF (371 kB) Citations (4)
References:
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.
Funding agency Grant number
Russian Science Foundation 19–11–00023
This work was performed at the MIPT and was supported by the Russian Science Foundation, project no. 19-11-00023.
Received: 28.06.2019
Revised: 05.12.2019
Accepted: 24.01.2020
English version:
Doklady Mathematics, 2020, Volume 101, Issue 2, Pages 169–171
DOI: https://doi.org/10.1134/S1064562420020167
Bibliographic databases:
Document Type: Article
UDC: 519.63
Language: Russian
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
Citation in format AMSBIB
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\transl
\jour Dokl. Math.
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  • This publication is cited in the following 4 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia
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