Standard

Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer. / Prokhorov, Dmitry; Reshetova, Galina; Bratchikov, Denis.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2024. стр. 334-351 23 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 14817 LNCS).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаяРецензирование

Harvard

Prokhorov, D, Reshetova, G & Bratchikov, D 2024, Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer. в Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 23, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 14817 LNCS, Springer Science and Business Media Deutschland GmbH, стр. 334-351, 24th International Conference on Computational Science and Its Applications, Hanoi, Вьетнам, 01.07.2024. https://doi.org/10.1007/978-3-031-65238-7_23

APA

Prokhorov, D., Reshetova, G., & Bratchikov, D. (2024). Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer. в Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (стр. 334-351). [23] (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 14817 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-65238-7_23

Vancouver

Prokhorov D, Reshetova G, Bratchikov D. Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer. в Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH. 2024. стр. 334-351. 23. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-65238-7_23

Author

Prokhorov, Dmitry ; Reshetova, Galina ; Bratchikov, Denis. / Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2024. стр. 334-351 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{63ca9a7ecbde443bb9c4a79a3cc96428,
title = "Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer",
abstract = "The paper presents an approach for reconstructing the properties of two-dimensional viscoelastic medium with defined geometry using the simulated annealing algorithm. The inverse problem solution requires a lot of computational resources because the direct seismic modeling is performed at each iteration. The staggered grid finite-difference scheme is implemented using CUDA technology to speed up the solution of the direct problem by parallelization. The choice of the simulated annealing method for solving inverse problem is due to the method{\textquoteright}s ability to avoid local minima of the target functional. However, the simulated annealing method needs a good coverage of the model space by realizations of random probing vectors. It leads to enormous computation time in the case of a four-layer medium with elliptical inclusion in the third layer, which has 37 parameters. Therefore, the sequential reconstruction of model parameters, where the simulated annealing algorithm searches for the parameters in 1 or 2-D subspace, is introduced. Nevertheless, the attenuation properties of medium were not reconstructed by simulated annealing. For their study, the deep convolutional neural network is used.",
keywords = "seismic modeling, simulated annealing, viscoelastic medium",
author = "Dmitry Prokhorov and Galina Reshetova and Denis Bratchikov",
note = "The research was supported by the Russian Science Foundation grant no. 19–77-20004.; 24th International Conference on Computational Science and Its Applications, ICCSA 2024 ; Conference date: 01-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1007/978-3-031-65238-7_23",
language = "English",
isbn = "9783031652370",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "334--351",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",

}

RIS

TY - GEN

T1 - Simulated Annealing Algorithm for Model Reconstruction of the Four-Layer Medium with Elliptical Inclusion in the Third Layer

AU - Prokhorov, Dmitry

AU - Reshetova, Galina

AU - Bratchikov, Denis

N1 - Conference code: 24

PY - 2024

Y1 - 2024

N2 - The paper presents an approach for reconstructing the properties of two-dimensional viscoelastic medium with defined geometry using the simulated annealing algorithm. The inverse problem solution requires a lot of computational resources because the direct seismic modeling is performed at each iteration. The staggered grid finite-difference scheme is implemented using CUDA technology to speed up the solution of the direct problem by parallelization. The choice of the simulated annealing method for solving inverse problem is due to the method’s ability to avoid local minima of the target functional. However, the simulated annealing method needs a good coverage of the model space by realizations of random probing vectors. It leads to enormous computation time in the case of a four-layer medium with elliptical inclusion in the third layer, which has 37 parameters. Therefore, the sequential reconstruction of model parameters, where the simulated annealing algorithm searches for the parameters in 1 or 2-D subspace, is introduced. Nevertheless, the attenuation properties of medium were not reconstructed by simulated annealing. For their study, the deep convolutional neural network is used.

AB - The paper presents an approach for reconstructing the properties of two-dimensional viscoelastic medium with defined geometry using the simulated annealing algorithm. The inverse problem solution requires a lot of computational resources because the direct seismic modeling is performed at each iteration. The staggered grid finite-difference scheme is implemented using CUDA technology to speed up the solution of the direct problem by parallelization. The choice of the simulated annealing method for solving inverse problem is due to the method’s ability to avoid local minima of the target functional. However, the simulated annealing method needs a good coverage of the model space by realizations of random probing vectors. It leads to enormous computation time in the case of a four-layer medium with elliptical inclusion in the third layer, which has 37 parameters. Therefore, the sequential reconstruction of model parameters, where the simulated annealing algorithm searches for the parameters in 1 or 2-D subspace, is introduced. Nevertheless, the attenuation properties of medium were not reconstructed by simulated annealing. For their study, the deep convolutional neural network is used.

KW - seismic modeling

KW - simulated annealing

KW - viscoelastic medium

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85200722318&origin=inward&txGid=9891399a30457b117d69f958715630d6

UR - https://www.mendeley.com/catalogue/bcf9a249-3df8-33f4-88ca-b29453b777b9/

U2 - 10.1007/978-3-031-65238-7_23

DO - 10.1007/978-3-031-65238-7_23

M3 - Conference contribution

SN - 9783031652370

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 334

EP - 351

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th International Conference on Computational Science and Its Applications

Y2 - 1 July 2024 through 4 July 2024

ER -

ID: 60494695