S.V. Zaycev, R.D. Ahmetsafin, S.A. Budennyj, S. Zhuravlev, K.V. Kiselev, R. V. Orlov, A. S. Smelov, G.S. Grigorev, V. Gulin, V. Ananev
{"title":"基于特征选择的非地震方法数据结构边界重建的机器学习算法","authors":"S.V. Zaycev, R.D. Ahmetsafin, S.A. Budennyj, S. Zhuravlev, K.V. Kiselev, R. V. Orlov, A. S. Smelov, G.S. Grigorev, V. Gulin, V. Ananev","doi":"10.3997/2214-4609.202156005","DOIUrl":null,"url":null,"abstract":"Summary Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.","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":"1","resultStr":"{\"title\":\"Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selection\",\"authors\":\"S.V. Zaycev, R.D. Ahmetsafin, S.A. Budennyj, S. Zhuravlev, K.V. Kiselev, R. V. Orlov, A. S. Smelov, G.S. Grigorev, V. Gulin, V. Ananev\",\"doi\":\"10.3997/2214-4609.202156005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.\",\"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\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science in Oil and Gas 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.202156005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science in Oil and Gas 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202156005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selection
Summary Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.