Application of Convolutional Neural Networks in Inverse Problems of Geoelectrics

IF 0.9 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS
M. I. Shimelevich, E. A. Rodionov, I. E. Obornev, E. A. Obornev
{"title":"Application of Convolutional Neural Networks in Inverse Problems of Geoelectrics","authors":"M. I. Shimelevich,&nbsp;E. A. Rodionov,&nbsp;I. E. Obornev,&nbsp;E. A. Obornev","doi":"10.1134/S1069351324701039","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b>—Neural networks (NNs) are successfully used to solve inverse and other problems in geophysics. The aim of this work, which is a continuation of a series of works by a group of authors, is to improve the efficiency of the NN method for solving nonlinear inverse 3D problems of geoelectrics, based on the construction of the author’s convolutional neural network. The network includes a number of additional special transformations (data compression, suppression of the influence of an unknown background environment, etc.) preceding the training of a classical MLP neural network and adapted to the inverse problem that is being solved. This allows us to formally, excluding the human factor, solve inverse problems of geoelectrics of large dimensions without specifying a first approximation based on data measured in areas whose dimensions exceed the dimensions of the network training area. The inversion speed is a few tens of seconds and does not depend on the physical dimensionality (2D or 3D) of the data. The solution to the inverse problem found using a trained neural network can, if necessary, be refined using a random search method. Numerical results of solving 3D geoelectric problems on model and field data are presented, confirming the stated development parameters.</p>","PeriodicalId":602,"journal":{"name":"Izvestiya, Physics of the Solid Earth","volume":"60 6","pages":"1215 - 1227"},"PeriodicalIF":0.9000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Izvestiya, Physics of the Solid Earth","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1134/S1069351324701039","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract—Neural networks (NNs) are successfully used to solve inverse and other problems in geophysics. The aim of this work, which is a continuation of a series of works by a group of authors, is to improve the efficiency of the NN method for solving nonlinear inverse 3D problems of geoelectrics, based on the construction of the author’s convolutional neural network. The network includes a number of additional special transformations (data compression, suppression of the influence of an unknown background environment, etc.) preceding the training of a classical MLP neural network and adapted to the inverse problem that is being solved. This allows us to formally, excluding the human factor, solve inverse problems of geoelectrics of large dimensions without specifying a first approximation based on data measured in areas whose dimensions exceed the dimensions of the network training area. The inversion speed is a few tens of seconds and does not depend on the physical dimensionality (2D or 3D) of the data. The solution to the inverse problem found using a trained neural network can, if necessary, be refined using a random search method. Numerical results of solving 3D geoelectric problems on model and field data are presented, confirming the stated development parameters.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Izvestiya, Physics of the Solid Earth
Izvestiya, Physics of the Solid Earth 地学-地球化学与地球物理
CiteScore
1.60
自引率
30.00%
发文量
60
审稿时长
6-12 weeks
期刊介绍: Izvestiya, Physics of the Solid Earth is an international peer reviewed journal that publishes results of original theoretical and experimental research in relevant areas of the physics of the Earth''s interior and applied geophysics. The journal welcomes manuscripts from all countries in the English or Russian language.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信