Vol. 12, no.2, 2020
РусскийEnglish
INFORMATION TECHNOLOGIES
Inverse problems of macrofracture formations exploration seismology solution with use of convolutional neural networks
Maxim V. Muratov, Vasily V. Ryazanov, Igor B. Petrov
Moscow Institute of Physics and Technology, https://mipt.ru/
Dolgoprudnyi 141700, Moscow region, Russian Federation
E-mail: max.muratov@gmail.com, vassavadda@gmail.com, petrov@mipt.ru
Received July 3, 2020, peer reviewed July 07, 2020, accepted July 08, 2020
Abstract. This article is devoted to solving the inverse problems of exploration seismology of uniformly oriented macrofractures systems using convolutional neural networks. The use of convolutional neural networks is optimal due to the multidimensionality of the studied data object. A training sample was formed using mathematical modeling. In the numerical solution of direct problems, a grid-characteristic method with interpolation on unstructured triangular meshes was used to form a training sample. The grid-characteristic method most accurately describes the dynamic processes in exploration seismology problems, since it takes into account the nature of wave phenomena. The approach used makes it possible to construct correct computational algorithms at the boundaries and contact boundaries of the integrational domain. Fractures were set discretely in the integration domain in the form of boundaries and contact boundaries. The article presents the results of solving inverse problems with variations in the angle of inclination of fractures, height of fractures, density of fractures in the system, as well as joint variations in the angle of inclination and height of fractures and all three investigated parameters.
Keywords: machine learning, convolutional neural networks, mathematical modeling, grid-characteristic method, exploration seismology, inverse problems, fractured media
UDC 004.93
RENSIT, 2020, 12(2):253-262. DOI: 10.17725/rensit.2020.12.253.
Full-text electronic version of this article - web site http://en.rensit.ru/vypuski/article/332/12(2)253-262e.pdf