Cooperative deep learning inversion: Seismic-constrained CSEM inversion for salt delineation

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Cooperative deep learning inversion: Seismic-constrained CSEM inversion for salt delineationAuthors: Seokmin OhKyubo NohDaeung YoonSoon JeeJoongmoo ByunSeokmin OhRISE.ML, Hanyang UniversitySearch for more papers by this author, Kyubo NohRISE.ML, Hanyang UniversitySearch for more papers by this author, Daeung YoonRISE.ML, Hanyang UniversitySearch for more papers by this author, Soon JeeRISE.ML, Hanyang UniversitySearch for more papers by this author, and Joongmoo ByunRISE.ML, Hanyang UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3208029.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSalt structure imaging is one of the most important problems in the field of hydrocarbon exploration. To resolve this issue, the integration of diverse geophysical data has emerged. In this study, we proposed the cooperative inversion with seismic and controlled-source electromagnetic (CSEM) data based on the supervised deep learning (DL) technique for precise salt delineation. CSEM data, which are effective in distinguishing a salt body with high electrical resistivity from the surrounding media, were used as the data of the inversion, and a high-resolution information derived from seismic data was applied as the constraint. To combine the seismic constraint with CSEM data, the modified UNet was adopted as an inversion operator based on DL. For training the DL model based on the network, resistivity models, including a salt body with arbitrary shape and size, and the corresponding CSEM data calculated through numerical modeling were generated and used as the label and input data, respectively. In addition, the seismic constraints, which were supposed to be obtained from the seismic image, were provided to the DL model in the training phase. Finally, we applied the optimum model to the test data acquired using the modified SEAM model. Test results demonstrated that the integration of seismic constraint leads to enhanced delineation of the salt body by providing definite upper boundary. This study has presented the promising potential of DL inversion to integrate multiple geophysical data.Presentation Date: Tuesday, September 17, 2019Session Start Time: 1:50 PMPresentation Time: 2:40 PMLocation: 225CPresentation Type: OralKeywords: electromagnetics, machine learning, inversion, salt, resistivityPermalink: https://doi.org/10.1190/segam2019-3208029.1FiguresReferencesRelatedDetailsCited byJoint 3D inversion of gravity and magnetic data using deep learning neural networksNanyu Wei, Dikun Yang, Zhigang Wang, and Yao Lu15 August 2022Monitoring the integrity of steel well casings using electrical data on the surfaceYinchu Li and Dikun Yang30 December 2020Imaging of steel casing’s conductivity using surface electrical data and a deep learning approachYinchu Li and Dikun Yang30 September 2020Deep learning joint inversion of seismic and electromagnetic data for salt reconstructionYen Sun, Bertrand Denel, Norman Daril, Lory Evano, Paul Williamson, and Mauricio Araya-Polo30 September 2020Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineationSeokmin Oh, Kyubo Noh, Soon Jee Seol, and Joongmoo Byun10 June 2020 | GEOPHYSICS, Vol. 85, No. 4 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 Seokmin Oh, Kyubo Noh, Daeung Yoon, Soon Jee Seol, and Joongmoo Byun, (2019), "Cooperative deep learning inversion: Seismic-constrained CSEM inversion for salt delineation," SEG Technical Program Expanded Abstracts : 1055-1059. https://doi.org/10.1190/segam2019-3208029.1 Plain-Language Summary Keywordselectromagneticsmachine learninginversionsaltresistivityPDF DownloadLoading ...