Estimating total magnetization directions using convolutional neural networks

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Estimating total magnetization directions using convolutional neural networksAuthors: Felicia NurindrawatiJiajia SunFelicia NurindrawatiDepartment of Earth and Atmospheric Sciences, University of HoustonSearch for more papers by this author and Jiajia SunDepartment of Earth and Atmospheric Sciences, University of HoustonSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216857.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractProper interpretation of magnetic data requires an accurate knowledge of total magnetization directions of the source bodies in an area of study. In this study, we examined the use of machine learning, specifically Convolutional Neural Network (CNN), to automatically predict the magnetization direction of a magnetic source body based on a magnetic map. We simulated magnetic data maps with varying magnetization directions from a cubic source body, all subject to the same inducing field. Two CNNs were trained separately, one for predicting magnetization inclinations and the other for predicting magnetization declinations. We also investigated various CNN architectures and determined the optimal architectures for predicting inclinations and declinations. For the optimal architectures, we achieved 98% and 100% test accuracy for our declination and inclination predictors, respectively. Furthermore, this method was tested on magnetic field data from Black Hill Norite, Australia, with encouraging results. Our study shows that machine learning holds great promise for automatically predicting magnetization directions based on magnetic data maps.Presentation Date: Wednesday, September 18, 2019Session Start Time: 8:30 AMPresentation Time: 8:55 AMLocation: 301BPresentation Type: OralKeywords: machine learning, magnetization, magnetics, neural networks, AustraliaPermalink: https://doi.org/10.1190/segam2019-3216857.1FiguresReferencesRelatedDetailsCited byDeep-learning seismic full-waveform inversion for realistic structural modelsBin Liu, Senlin Yang, Yuxiao Ren, Xinji Xu, Peng Jiang, and Yangkang Chen4 January 2021 | GEOPHYSICS, Vol. 86, No. 1Improving the accuracy of convolutional neural networks in predicting magnetization directionsFelicia Nurindrawati and Jiajia Sun30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Felicia Nurindrawati and Jiajia Sun, (2019), "Estimating total magnetization directions using convolutional neural networks," SEG Technical Program Expanded Abstracts : 2163-2167. https://doi.org/10.1190/segam2019-3216857.1 Plain-Language Summary Keywordsmachine learningmagnetizationmagneticsneural networksAustraliaPDF DownloadLoading ...