SEISMIC DATA INTERPOLATION USING ATTENTION-BASED DEEP LEARNING
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jongjoo | - |
dc.contributor.author | Yeeh, Zeu | - |
dc.contributor.author | Seol, Soonjee | - |
dc.contributor.author | Byun, Joongmoo | - |
dc.date.accessioned | 2022-12-20T10:37:20Z | - |
dc.date.available | 2022-12-20T10:37:20Z | - |
dc.date.created | 2022-12-07 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173243 | - |
dc.description.abstract | Seismic data suffer from insufficient spatial data sampling due to many restrictions. Therefore, seismic data interpolation is demanded as pre-processing on data processing to enhance data quality. Despite of wide usage of conventional seismic data interpolation methods, it is still challenging to interpolate where the data has consecutive missing. In order to improve interpolation performance at consecutive large gap, we propose a convolutional neural network equipped attention mechanism, called CUNet. We test the performance of proposed method using seismic field data. We also compare the results of CUNet with results of MWNI and UNet methods to verify the reliability of the proposed method. The test results show that CUNet is robust to restore the amplitude at consecutive traces missing. It indicates that the proposed method is feasible to interpolate even where the traces gap is relatively large. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | European Association of Geoscientists and Engineers, EAGE | - |
dc.title | SEISMIC DATA INTERPOLATION USING ATTENTION-BASED DEEP LEARNING | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Byun, Joongmoo | - |
dc.identifier.doi | 10.3997/2214-4609.202210245 | - |
dc.identifier.scopusid | 2-s2.0-85142606043 | - |
dc.identifier.bibliographicCitation | 83rd EAGE Conference and Exhibition 2022, v.2, pp.937 - 941 | - |
dc.relation.isPartOf | 83rd EAGE Conference and Exhibition 2022 | - |
dc.citation.title | 83rd EAGE Conference and Exhibition 2022 | - |
dc.citation.volume | 2 | - |
dc.citation.startPage | 937 | - |
dc.citation.endPage | 941 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Data handling | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Geophysical prospecting | - |
dc.subject.keywordPlus | Seismic response | - |
dc.subject.keywordPlus | Seismic waves | - |
dc.subject.keywordPlus | Interpolation | - |
dc.subject.keywordPlus | Attention mechanisms | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Data interpolation | - |
dc.subject.keywordPlus | Data quality | - |
dc.subject.keywordPlus | Data sampling | - |
dc.subject.keywordPlus | Interpolation method | - |
dc.subject.keywordPlus | Performance | - |
dc.subject.keywordPlus | Pre-processing | - |
dc.subject.keywordPlus | Seismic datas | - |
dc.subject.keywordPlus | Spatial data | - |
dc.identifier.url | https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210245 | - |
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