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dc.contributor.authorYu, Han
dc.contributor.authorChen, Yuqing
dc.contributor.authorHanafy, Sherif M.
dc.contributor.authorSchuster, Gerard T.
dc.date.accessioned2021-01-28T06:22:41Z
dc.date.available2021-01-28T06:22:41Z
dc.date.issued2021-01-09
dc.date.submitted2020-03-01
dc.identifier.citationYu, H., Chen, Y., Hanafy, S. M., & Schuster, G. T. (2021). Skeletonized Wave-Equation Refraction Inversion With Autoencoded Waveforms. IEEE Transactions on Geoscience and Remote Sensing, 1–18. doi:10.1109/tgrs.2020.3046093
dc.identifier.issn1558-0644
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/TGRS.2020.3046093
dc.identifier.urihttp://hdl.handle.net/10754/667069
dc.description.abstractWe present a method that skeletonizes the first arriving seismic refractions by machine learning and inverts them for the subsurface velocity model. In this study, first arrivals can be compressed in a low-rank sense with their skeletal features extracted by a well-trained autoencoder. Empirical experiments suggest that the autoencoder's 1x1 or 2x1 latent vectors vary continuously with respect to the input seismic data. It is, therefore, reasonable to introduce a misfit functional measuring the discrepancies between the predicted and the observed data in a low-dimensional latent space. The benefit of this approach is that an elaborated autoencoding neural network not only refines intrinsic information hidden in the refractions but also improves the quality of inversion for a reliable background velocity model. Numerical tests on both synthetic and field data demonstrate the effectiveness of this method, especially in recovering the low-to-intermediate wavenumber parts of the subsurface velocity distribution. Comparisons are made with the other three relevant methods, the wave-equation travel-time (WT) inversion, the envelope inversion, and the full waveform inversion (FWI). As expected, the cycle skipping problem is alleviated due to the reduction of dimensions of data space. This method outperforms the envelope inversion in resolution, and it is no worse than WT. Moreover, there is no need for careful manual travel-time picking with this methodology. In general, this inversion framework provides an extendable strategy to compress any input data for reconstructing high-dimensional physical parameters.
dc.description.sponsorshipThe authors are very grateful to the anonymous reviewers for their precious comments and suggestions.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9316878/
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleSkeletonized Wave-Equation Refraction Inversion With Autoencoded Waveforms
dc.typeArticle
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.identifier.journalIEEE Transactions on Geoscience and Remote Sensing
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China, also with the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China, and also with Suzhou Keda Technology Company Ltd., Suzhou 215011, China .
dc.contributor.institutionCollege of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
dc.identifier.pages1-18
kaust.personChen, Yuqing
kaust.personSchuster, Gerard T.
dc.date.accepted2020-12-13
dc.identifier.eid2-s2.0-85099551865
refterms.dateFOA2021-01-31T06:53:43Z
dc.date.published-online2021-01-09
dc.date.published-print2021-10


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