Coarse-refine Network with Upsampling Techniques and Fourier Loss for the Reconstruction of Missing Seismic Data SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Park, Hanjoon | - |
dc.contributor.author | Lee, Jun-Woo | - |
dc.contributor.author | Hwang, Jongha | - |
dc.contributor.author | Min, Dong-Joo | - |
dc.date.accessioned | 2022-07-25T04:30:00Z | - |
dc.date.available | 2022-07-25T04:30:00Z | - |
dc.date.created | 2022-07-25 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.issn | 0196-2892 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/43103 | - |
dc.description.abstract | Seismic data are often irregularly or insufficiently sampled along the spatial direction due to malfunctioning of receivers and limited survey budgets. Recently, machine learning techniques have begun to be used to effectively reconstruct missing traces and obtain densely sampled seismic gathers. One of the most widely used machine learning techniques for seismic trace interpolation is UNet with the mean-squared error (MSE). However, seismic trace interpolation with the UNet architecture suffers from aliasing, and the MSE used as a loss function causes an oversmoothing problem. To mitigate those problems in seismic trace interpolation, we propose a new strategy of using coarse-refine UNet (CFunet) and the Fourier loss. CFunet consists of two UNets and an upsampling process between them. The upsampling process is done by padding zeroes in the Fourier domain. We design the new loss function by combining the MSE and the Fourier loss. Unlike the MSE, the Fourier loss is not a pixelwise loss but plays a role in capturing relations between pixels. Synthetic and field data experiments show that the proposed method reduces aliased features and precisely reconstructs missing traces while accelerating the convergence of the network. By applying our strategy to realistic cases, we show that our strategy can be applied to obtain more densely sampled data from acquired data. IEEE | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | Coarse-refine Network with Upsampling Techniques and Fourier Loss for the Reconstruction of Missing Seismic Data | - |
dc.type | Article | - |
dc.citation.endPage | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | IEEE Transactions on Geoscience and Remote Sensing | - |
dc.citation.volume | 60 | - |
dc.contributor.alternativeName | 황종하 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Geoscience and Remote Sensing, v.60, pp.1 - 1 | - |
dc.identifier.doi | 10.1109/TGRS.2022.3190292 | - |
dc.identifier.scopusid | 2-s2.0-85134201571 | - |
dc.identifier.wosid | 000829062600015 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Coarse-refine network | - |
dc.subject.keywordAuthor | Convergence | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Fourier loss | - |
dc.subject.keywordAuthor | Fourier transform | - |
dc.subject.keywordAuthor | Image reconstruction | - |
dc.subject.keywordAuthor | Interpolation | - |
dc.subject.keywordAuthor | Neural networks | - |
dc.subject.keywordAuthor | Seismic data interpolation | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | UNet | - |
dc.relation.journalWebOfScienceCategory | Geochemistry & Geophysics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Geochemistry & Geophysics | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |