File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)

Article: Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

TitleJoint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint
Authors
KeywordsAudio-magnetotelluric (AMT)
deep learning (DL)
joint inversionresistivity
travel time
velocity
Issue Date2021
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59 n. 9, p. 7982-7995 How to Cite?
AbstractDeep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity–velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss–Newton method, based on the reference model generated by the trained DRCNNs. The workflow of this joint inversion scheme and the design of the DRCNNs are explained in detail. Compared with describing the resistivity–velocity relationship using empirical equations, this method can avoid the necessity in modeling the correlations in rigorous mathematical forms and extract more hidden prior information embedded in the training set, meanwhile preserving the structural similarity between different inverted models. Numerical tests show that the inverted resistivity and velocity have similar profiles, and their relationship can be kept consistent with the prior joint distribution. Furthermore, the convergence is faster, and final data misfits can be lower than separate inversion.
Persistent Identifierhttp://hdl.handle.net/10722/302061
ISSN
2020 Impact Factor: 5.6
2020 SCImago Journal Rankings: 2.141

 

DC FieldValueLanguage
dc.contributor.authorRuo, R-
dc.contributor.authorYao, H-
dc.contributor.authorLi, M-
dc.contributor.authorNg, MKP-
dc.contributor.authorJiang, L-
dc.contributor.authorAbubakar, A-
dc.date.accessioned2021-08-21T03:31:00Z-
dc.date.available2021-08-21T03:31:00Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59 n. 9, p. 7982-7995-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/302061-
dc.description.abstractDeep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity–velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss–Newton method, based on the reference model generated by the trained DRCNNs. The workflow of this joint inversion scheme and the design of the DRCNNs are explained in detail. Compared with describing the resistivity–velocity relationship using empirical equations, this method can avoid the necessity in modeling the correlations in rigorous mathematical forms and extract more hidden prior information embedded in the training set, meanwhile preserving the structural similarity between different inverted models. Numerical tests show that the inverted resistivity and velocity have similar profiles, and their relationship can be kept consistent with the prior joint distribution. Furthermore, the convergence is faster, and final data misfits can be lower than separate inversion.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.rightsIEEE Transactions on Geoscience and Remote Sensing. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.-
dc.subjectAudio-magnetotelluric (AMT)-
dc.subjectdeep learning (DL)-
dc.subjectjoint inversionresistivity-
dc.subjecttravel time-
dc.subjectvelocity-
dc.titleJoint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint-
dc.typeArticle-
dc.identifier.emailYao, H: yaohm@hku.hk-
dc.identifier.emailNg, MKP: michael.ng@hku.hk-
dc.identifier.authorityNg, MKP=rp02578-
dc.identifier.authorityJiang, L=rp01338-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2020.3032743-
dc.identifier.hkuros324393-
dc.identifier.volume59-
dc.identifier.issue9-
dc.identifier.spage7982-
dc.identifier.epage7995-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats