Approximation of Boundary Condition in Higher Order Grid-Characteristic Schemes

Petrov, I. B.; Golubev, V. I.; Shevchenko, A. V.; Nikitin, I. S.

doi:10.1134/s1064562423701375

Dokl. Math.

2023

DOI: 10.1134/s1064562423701375

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Approximation of Boundary Condition in Higher Order Grid-Characteristic Schemes

I. B. Petrov,

V. I. Golubev,

A. V. Shevchenko

et al.

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2024

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Cited by 1 publication

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Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Golubev,

Anisimov

2024

Int. J. Appl. Mechanics

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Seismic inversion is an important part of the modern geological exploration process. Novel applications of deep learning are capable of handling heterogeneous media, but require too much data for training. In this paper, we focus on the prediction of fracture inclusion location and its parameters in rock media and approach the problem in the multi-task manner. For this, several multi-task convolutional neural network (CNN) architectures are proposed and compared. The direct seismic problem is considered in the heterogeneous fractured geological model based on the well-known Marmousi2 model in a two-dimensional case. The model of the deformable solid medium containing slip planes with nonlinear slip conditions on them and explicit–implicit numerical method is applied to obtain the synthetic seismic dataset for CNN training and validation.

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Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Golubev,

Anisimov

2024

Int. J. Appl. Mechanics

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show abstract

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Approximation of Boundary Condition in Higher Order Grid-Characteristic Schemes

Cited by 1 publication

References 17 publications

Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

Application of Convolutional Networks for Localization and Prediction of Scalar Parameters of Fractured Geological Inclusion

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