Automatic computation of relative geologic time volume using self-supervised learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dowan | - |
dc.contributor.author | Byun, Joongmoo | - |
dc.date.accessioned | 2022-07-06T12:12:30Z | - |
dc.date.available | 2022-07-06T12:12:30Z | - |
dc.date.created | 2022-01-05 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1052-3812 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140961 | - |
dc.description.abstract | Although relative geologic time (RGT) volume is an attribute highly utilized in seismic interpretation, accurate automatic prediction of RGT volume is very difficult. In this study, we developed the self-supervised learning-based algorithm, which can generate a RGT volume without labels. To replace the labels, we have proposed the new task using cycle-consistent tracking, which can train the machine learning network using only seismic images. The proposed algorithm has the advantage of generating self-supervision by itself and automatically generating RGT volumes without user's supervision. We have validated the developed algorithm using the Glencoe field data. The estimated results showed that the relatively reliable RGT volumes were predicted even in complex images containing discontinuous structures. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Society of Exploration Geophysicists | - |
dc.title | Automatic computation of relative geologic time volume using self-supervised learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Byun, Joongmoo | - |
dc.identifier.doi | 10.1190/segam2021-3581832.1 | - |
dc.identifier.scopusid | 2-s2.0-85120951606 | - |
dc.identifier.bibliographicCitation | SEG Technical Program Expanded Abstracts, v.2021-September, pp.1141 - 1145 | - |
dc.relation.isPartOf | SEG Technical Program Expanded Abstracts | - |
dc.citation.title | SEG Technical Program Expanded Abstracts | - |
dc.citation.volume | 2021-September | - |
dc.citation.startPage | 1141 | - |
dc.citation.endPage | 1145 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Geology | - |
dc.subject.keywordPlus | Supervised learning | - |
dc.subject.keywordPlus | Automatic computations | - |
dc.subject.keywordPlus | Automatic prediction | - |
dc.subject.keywordPlus | Complex image | - |
dc.subject.keywordPlus | Field data | - |
dc.subject.keywordPlus | Seismic image | - |
dc.subject.keywordPlus | Seismic interpretation | - |
dc.subject.keywordPlus | User supervisions | - |
dc.subject.keywordPlus | Seismology | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.