Geodesic
Application of convolutional neural networks for search and determination of physical characteristics of inhomogeneities in geological media from seismic data
Matematičeskie zametki SVFU, Tome 30 (2023) no. 1, pp. 101-113 Cet article a éte moissonné depuis la source Math-Net.Ru

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With the use of convolutional neural networks, we solve inverse problems of exploration seismology to determine the spatial position and physical characteristics of geological fractures, such as the proportion of excess surface and the nature of saturation. The training and validation sets were formed using numerical modeling by the grid-characteristic method on unstructured meshes in the two-dimensional case. The continuum mechanics equations were used, while the fractures were speci ed discretely in the integration domain; this approach made it possible to obtain the most detailed patterns of wave responses.
Keywords: inverse problems of exploration seismology, fractured media, convolutional neural networks, machine learning, mathematical modeling, grid-characteristic method, discrete fractured models, infinitely thin fracture. 
@article{SVFU_2023_30_1_a7,
     author = {M. V. Muratov and D. S. Konov and D. I. Petrov and I. B. Petrov},
     title = {Application of convolutional neural networks for search and determination of physical characteristics of inhomogeneities in geological media from seismic data},
     journal = {Matemati\v{c}eskie zametki SVFU},
     pages = {101--113},
     year = {2023},
     volume = {30},
     number = {1},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/SVFU_2023_30_1_a7/}
}
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M. V. Muratov; D. S. Konov; D. I. Petrov; I. B. Petrov. Application of convolutional neural networks for search and determination of physical characteristics of inhomogeneities in geological media from seismic data. Matematičeskie zametki SVFU, Tome 30 (2023) no. 1, pp. 101-113. http://geodesic.mathdoc.fr/item/SVFU_2023_30_1_a7/