Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

被引:1
|
作者
Glinskikh, Viacheslav [1 ]
Nechaev, Oleg [1 ]
Mikhaylov, Igor [1 ]
Nikitenko, Marina [1 ]
Danilovskiy, Kirill [1 ]
机构
[1] RAS, Geophys Div, Multiscale Geophys Lab, Trofimuk Inst Petr Geol & Geophys,SB, Novosibirsk 630090, Russia
基金
俄罗斯科学基金会;
关键词
permafrost; TEM monitoring; Sumudu transform; vector finite element method; artificial neural networks; ELECTRICAL-RESISTIVITY TOMOGRAPHY; GROUND-PENETRATING RADAR; INVERSION; FIELD; 3D; FOUNDATIONS; DYNAMICS; SYSTEM;
D O I
10.3390/math12040585
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. We propose a cross-borehole exploration system for a high localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We consider numerical simulation results of the transient electromagnetic monitoring of industrial facilities located on permafrost. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.
引用
收藏
页数:24
相关论文
共 50 条
  • [41] Impedance-based structural health monitoring with artificial neural networks
    Lopes, V
    Park, G
    Cudney, HH
    Inman, DJ
    JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 2000, 11 (03) : 206 - 214
  • [42] Mathematical modeling of drying kinetics of ground Açaí (Euterpe oleracea) kernel using artificial neural networks
    Bannoud, Mohamad A.
    Gomes, Beatriz P.
    Abdalla, Marcela C. de S. P.
    Freire, Mariana V.
    Andreola, Kaciane
    Martins, Tiago D.
    da Silva, Carlos A. M.
    de Souza, Luciane F. G.
    Braga, Matheus B.
    CHEMICAL PAPERS, 2024, 78 (02) : 1033 - 1054
  • [43] Mathematical modeling of drying kinetics of ground Açaí (Euterpe oleracea) kernel using artificial neural networks
    Mohamad A. Bannoud
    Beatriz P. Gomes
    Marcela C. de S. P. Abdalla
    Mariana V. Freire
    Kaciane Andreola
    Tiago D. Martins
    Carlos A. M. da Silva
    Luciane F. G. de Souza
    Matheus B. Braga
    Chemical Papers, 2024, 78 : 1033 - 1054
  • [44] USE OF MATHEMATICAL MODELING (ARTIFICIAL NEURAL NETWORKS) IN CLASSIFICATION OF BANANA AUTOTETRAPLOID (Musa acuminata COLLA)
    de Oliveira, Ana Catarina Lima
    Pasqual, Moacir
    Salles Pio, Leila Aparecida
    Lacerda, Wilian Soares
    de Oliveira e Silva, Sebastiao
    BIOSCIENCE JOURNAL, 2013, 29 (03): : 616 - 621
  • [45] Modeling Electromagnetic Navigation Systems for Medical Applications using Random Forests and Artificial Neural Networks
    Yu, Ruoxi
    Charreyron, Samuel L.
    Boehler, Quentin
    Weibel, Cameron
    Chautems, Christophe
    Poon, Carmen C. Y.
    Nelson, Bradley J.
    2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2020, : 9251 - 9256
  • [46] Integration of industrial videogrammetry and artificial neural networks for monitoring and modeling the deformation or displacement of structures
    Ahmadi, Farshid Farnood
    NEURAL COMPUTING & APPLICATIONS, 2017, 28 (12): : 3709 - 3716
  • [47] Integration of industrial videogrammetry and artificial neural networks for monitoring and modeling the deformation or displacement of structures
    Farshid Farnood Ahmadi
    Neural Computing and Applications, 2017, 28 : 3709 - 3716
  • [48] Battery Consumption Modeling for Electric Vehicles Based on Artificial Neural Networks
    Lee, Junghoon
    Kang, Min-Jae
    Park, Gyung-Leen
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2014, PT IV, 2014, 8582 : 733 - +
  • [49] Cushion properties modeling of honeycombed paperboard based on artificial neural networks
    Zhou, Tingmei
    Mao, Ling
    Jisuanji Gongcheng/Computer Engineering, 2003, 29 (18):
  • [50] Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks
    Kalina, Karl A.
    Linden, Lennart
    Brummund, Jorg
    Metsch, Philipp
    Kastner, Markus
    COMPUTATIONAL MECHANICS, 2022, 69 (01) : 213 - 232