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dc.contributor.authorLiu, Zhaolun
dc.contributor.authorSchuster, Gerard T.
dc.date.accessioned2020-03-05T06:01:26Z
dc.date.available2020-03-05T06:01:26Z
dc.date.issued2019-08-10
dc.identifier.citationLiu, Z., & Schuster, G. (2019). Multilayer sparse LSM = deep neural network. SEG Technical Program Expanded Abstracts 2019. doi:10.1190/segam2019-3215033.1
dc.identifier.doi10.1190/segam2019-3215033.1
dc.identifier.urihttp://hdl.handle.net/10754/661893
dc.description.abstractWe recast the multilayered sparse inversion problem as a multilayered neural network problem. Unlike standard least squares migration (LSM) which finds the optimal reflectivity image, neural network least squares migration (NNLSM) finds both the optimal reflectivity image and the quasi-migration Green's functions. These quasi-migration Green's functions are also denoted as the convolutional filters in a convolutional neural network 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 migration images. Its disadvantage is that the NNLSM reflectivity image is only an approximation to the actual reflectivity distribution.
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/segam2019-3215033.1
dc.rightsArchived with thanks to Society of Exploration Geophysicists
dc.titleMultilayer sparse LSM=deep neural network
dc.typeConference Paper
dc.contributor.departmentCenter for Subsurface Imaging and Fluid Modeling
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.conference.date2019-09-15 to 2019-09-20
dc.conference.nameSociety of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019
dc.conference.locationSan Antonio, TX, USA
dc.eprint.versionPre-print
kaust.personLiu, Zhaolun
kaust.personSchuster, Gerard T.
refterms.dateFOA2020-03-08T06:07:42Z


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