I. Obornev, M. Shimelevich, E. Obornev, E. Rodionov
{"title":"Application of The Approximation Neural Network Method for Interpretation of Geoelectric Field Data","authors":"I. Obornev, M. Shimelevich, E. Obornev, E. Rodionov","doi":"10.3997/2214-4609.202156020","DOIUrl":null,"url":null,"abstract":"Summary The paper presents an example of the application of approximation neural network structures to the problem of reconstructing the resistivity distributions of 2D and 3D piecewise linear media from geoelectric data. This problem is reduced to solving a nonlinear operator equation of the first kind. An algorithm was proposed [ Shimelevich et al, 2018 , Obornev et al, 2020 ] for finding an approximate solution of this equation with a total number of parameters of the order of ∼ n 10 ^ 3, based on the use of neural (Kolmogorov) networks of the multilayer perceptron type. This approach, which allows real-time data inversion, is illustrated both on model examples and on profile and areal field survey data.","PeriodicalId":266953,"journal":{"name":"Data Science in Oil and Gas 2021","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science in Oil and Gas 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202156020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Summary The paper presents an example of the application of approximation neural network structures to the problem of reconstructing the resistivity distributions of 2D and 3D piecewise linear media from geoelectric data. This problem is reduced to solving a nonlinear operator equation of the first kind. An algorithm was proposed [ Shimelevich et al, 2018 , Obornev et al, 2020 ] for finding an approximate solution of this equation with a total number of parameters of the order of ∼ n 10 ^ 3, based on the use of neural (Kolmogorov) networks of the multilayer perceptron type. This approach, which allows real-time data inversion, is illustrated both on model examples and on profile and areal field survey data.
本文给出了一个应用近似神经网络结构求解二维和三维分段线性介质地电数据电阻率分布问题的实例。这个问题被简化为求解第一类非线性算子方程。提出了一种算法[Shimelevich等人,2018,Obornev等人,2020],基于多层感知器类型的神经(Kolmogorov)网络,用于寻找该方程的近似解,其参数总数为~ n 10 ^ 3的数量级。该方法可以实现实时数据反演,并在模型实例以及剖面和区域实地调查数据上进行了说明。