File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TGRS.2020.3032743
- WOS: WOS:000690968800065
- Find via
Supplementary
-
Citations:
- Web of Science: 0
- Appears in Collections:
Article: Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint
Title | Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint |
---|---|
Authors | |
Keywords | Audio-magnetotelluric (AMT) deep learning (DL) joint inversionresistivity travel time velocity |
Issue Date | 2021 |
Publisher | Institute 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? |
Abstract | Deep 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 Identifier | http://hdl.handle.net/10722/302061 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 2.403 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ruo, R | - |
dc.contributor.author | Yao, H | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Ng, MKP | - |
dc.contributor.author | Jiang, L | - |
dc.contributor.author | Abubakar, A | - |
dc.date.accessioned | 2021-08-21T03:31:00Z | - |
dc.date.available | 2021-08-21T03:31:00Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59 n. 9, p. 7982-7995 | - |
dc.identifier.issn | 0196-2892 | - |
dc.identifier.uri | http://hdl.handle.net/10722/302061 | - |
dc.description.abstract | Deep 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36 | - |
dc.relation.ispartof | IEEE Transactions on Geoscience and Remote Sensing | - |
dc.rights | IEEE 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.subject | Audio-magnetotelluric (AMT) | - |
dc.subject | deep learning (DL) | - |
dc.subject | joint inversionresistivity | - |
dc.subject | travel time | - |
dc.subject | velocity | - |
dc.title | Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint | - |
dc.type | Article | - |
dc.identifier.email | Yao, H: yaohm@hku.hk | - |
dc.identifier.email | Ng, MKP: michael.ng@hku.hk | - |
dc.identifier.authority | Ng, MKP=rp02578 | - |
dc.identifier.authority | Jiang, L=rp01338 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TGRS.2020.3032743 | - |
dc.identifier.hkuros | 324393 | - |
dc.identifier.volume | 59 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 7982 | - |
dc.identifier.epage | 7995 | - |
dc.identifier.isi | WOS:000690968800065 | - |
dc.publisher.place | United States | - |