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dc.contributor.authorLiu, Zhaolun
dc.contributor.authorChen, Yuqing
dc.contributor.authorSchuster, Gerard T.
dc.date.accessioned2021-03-24T09:22:47Z
dc.date.available2019-11-24T10:45:29Z
dc.date.available2021-03-24T09:22:47Z
dc.date.issued2020-06-02
dc.date.submitted2019-07-09
dc.identifier.citationLiu, Z., Chen, Y., & Schuster, G. (2020). Deep Convolutional Neural Network and Sparse Least Squares Migration. GEOPHYSICS, 1–57. doi:10.1190/geo2019-0412.1
dc.identifier.issn0016-8033
dc.identifier.issn1942-2156
dc.identifier.doi10.1190/geo2019-0412.1
dc.identifier.urihttp://hdl.handle.net/10754/660193
dc.description.abstractWe recast the forward pass of a multilayered convolutional neural network (CNN) as the solution to the problem of sparse least squares migration (LSM). The CNN filters and feature maps are shown to be analogous, but not equivalent, to the migration Green's functions and the quasi-reflectivity distribution, repsectively. This provides a physical interpretation of the filters and feature maps in deep CNN in terms of the operators for seismic imaging. Motivated by the connection between sparse LSM and CNN, we propose the neural network version of sparse LSM. Unlike the standard LSM method that finds the optimal reflectivity image, neural network LSM (NNLSM) finds both the optimal quasi-reflectivity image and the quasi-migration Green's functions. These quasi-migration-Green's functions are also denoted as the convolutional filters in a CNN and are similar to migration Green's functions. The advantage of NNLSM over standard LSM is that its computational cost is significantly less and it can be used for denoising coherent and incoherent noise in migration images. Its disadvantage is that the NNLSM quasi-reflectivity image is only an approximation to the actual reflectivity distribution. However, the quasi-reflectivity image can be used as a superresolution attribute image for high-resolution delineation of geologic bodies.
dc.language.isoen
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/geo2019-0412.1
dc.rightsArchived with thanks to GEOPHYSICS. © 2020 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See http://creativecommons.org/licenses/by/4.0/. Distribution or reproduction of this work in whole or in part commercially or noncommercially requires full attribution of the original publication, including its digital object identifier (DOI).
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeep Convolutional Neural Network and Sparse Least Squares Migration
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalGEOPHYSICS
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionPrinceton University, Department of Geosciences, Princeton, New Jersey 08544, USA..
dc.contributor.institutionCSIRO, Deep Earth Imaging Future Science Platform, Kensington, Australia.
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.pagesWA241-WA253
pubs.publication-statusPublished
dc.identifier.arxivid1904.09321
kaust.personSchuster, Gerard T.
dc.date.accepted2020-06-02
refterms.dateFOA2019-11-24T10:45:29Z


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Archived with thanks to GEOPHYSICS. © 2020 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See http://creativecommons.org/licenses/by/4.0/. Distribution or reproduction of this work in whole or in part commercially or noncommercially requires full attribution of the original publication, including its digital object identifier (DOI).
Except where otherwise noted, this item's license is described as Archived with thanks to GEOPHYSICS. © 2020 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See http://creativecommons.org/licenses/by/4.0/. Distribution or reproduction of this work in whole or in part commercially or noncommercially requires full attribution of the original publication, including its digital object identifier (DOI).
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