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JOURNALS // Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia // Archive

Dokl. RAN. Math. Inf. Proc. Upr., 2020 Volume 491, Pages 107–110 (Mi danma60)

This article is cited 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.

UDC: 519.63

Received: 28.06.2019
Revised: 05.12.2019
Accepted: 24.01.2020

DOI: 10.31857/S2686954320020162


 English version:
Doklady Mathematics, 2020, 101:2, 169–171

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