Matches in SemOpenAlex for { <https://semopenalex.org/work/W3094709780> ?p ?o ?g. }
- W3094709780 endingPage "7995" @default.
- W3094709780 startingPage "7982" @default.
- W3094709780 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." @default.
- W3094709780 created "2020-11-09" @default.
- W3094709780 creator A5003510248 @default.
- W3094709780 creator A5015375162 @default.
- W3094709780 creator A5032045741 @default.
- W3094709780 creator A5036046769 @default.
- W3094709780 creator A5079173460 @default.
- W3094709780 creator A5089388395 @default.
- W3094709780 date "2021-09-01" @default.
- W3094709780 modified "2023-10-17" @default.
- W3094709780 title "Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint" @default.
- W3094709780 cites W1005811612 @default.
- W3094709780 cites W1670714003 @default.
- W3094709780 cites W1967409080 @default.
- W3094709780 cites W1969923629 @default.
- W3094709780 cites W1974116881 @default.
- W3094709780 cites W1976784524 @default.
- W3094709780 cites W1977638890 @default.
- W3094709780 cites W2005424979 @default.
- W3094709780 cites W2020889088 @default.
- W3094709780 cites W2031401646 @default.
- W3094709780 cites W2069849147 @default.
- W3094709780 cites W2087452412 @default.
- W3094709780 cites W2090351291 @default.
- W3094709780 cites W2095767005 @default.
- W3094709780 cites W2098433339 @default.
- W3094709780 cites W2115957863 @default.
- W3094709780 cites W2122662657 @default.
- W3094709780 cites W2139065094 @default.
- W3094709780 cites W2155107551 @default.
- W3094709780 cites W2156852680 @default.
- W3094709780 cites W2168959606 @default.
- W3094709780 cites W2328624182 @default.
- W3094709780 cites W2332990274 @default.
- W3094709780 cites W2335468769 @default.
- W3094709780 cites W2345680641 @default.
- W3094709780 cites W2504960715 @default.
- W3094709780 cites W2574952845 @default.
- W3094709780 cites W2584866998 @default.
- W3094709780 cites W2745439097 @default.
- W3094709780 cites W2800441593 @default.
- W3094709780 cites W2894120608 @default.
- W3094709780 cites W2897204095 @default.
- W3094709780 cites W2902216690 @default.
- W3094709780 cites W2918772560 @default.
- W3094709780 cites W2946569826 @default.
- W3094709780 cites W2955306557 @default.
- W3094709780 cites W2956750940 @default.
- W3094709780 cites W2968271084 @default.
- W3094709780 cites W2968528446 @default.
- W3094709780 cites W2981658631 @default.
- W3094709780 cites W2983807332 @default.
- W3094709780 cites W3003964717 @default.
- W3094709780 cites W3004921325 @default.
- W3094709780 cites W3007939983 @default.
- W3094709780 cites W3012687401 @default.
- W3094709780 cites W3018733595 @default.
- W3094709780 cites W3027867949 @default.
- W3094709780 cites W3042203856 @default.
- W3094709780 cites W35311215 @default.
- W3094709780 doi "https://doi.org/10.1109/tgrs.2020.3032743" @default.
- W3094709780 hasPublicationYear "2021" @default.
- W3094709780 type Work @default.
- W3094709780 sameAs 3094709780 @default.
- W3094709780 citedByCount "25" @default.
- W3094709780 countsByYear W30947097802021 @default.
- W3094709780 countsByYear W30947097802022 @default.
- W3094709780 countsByYear W30947097802023 @default.
- W3094709780 crossrefType "journal-article" @default.
- W3094709780 hasAuthorship W3094709780A5003510248 @default.
- W3094709780 hasAuthorship W3094709780A5015375162 @default.
- W3094709780 hasAuthorship W3094709780A5032045741 @default.
- W3094709780 hasAuthorship W3094709780A5036046769 @default.
- W3094709780 hasAuthorship W3094709780A5079173460 @default.
- W3094709780 hasAuthorship W3094709780A5089388395 @default.
- W3094709780 hasConcept C108583219 @default.
- W3094709780 hasConcept C112313211 @default.
- W3094709780 hasConcept C11413529 @default.
- W3094709780 hasConcept C119599485 @default.
- W3094709780 hasConcept C127313418 @default.
- W3094709780 hasConcept C127413603 @default.
- W3094709780 hasConcept C13280743 @default.
- W3094709780 hasConcept C153180895 @default.
- W3094709780 hasConcept C154945302 @default.
- W3094709780 hasConcept C155512373 @default.
- W3094709780 hasConcept C165205528 @default.
- W3094709780 hasConcept C1893757 @default.
- W3094709780 hasConcept C41008148 @default.
- W3094709780 hasConcept C69990965 @default.
- W3094709780 hasConcept C77928131 @default.
- W3094709780 hasConcept C81363708 @default.
- W3094709780 hasConceptScore W3094709780C108583219 @default.
- W3094709780 hasConceptScore W3094709780C112313211 @default.
- W3094709780 hasConceptScore W3094709780C11413529 @default.
- W3094709780 hasConceptScore W3094709780C119599485 @default.
- W3094709780 hasConceptScore W3094709780C127313418 @default.
- W3094709780 hasConceptScore W3094709780C127413603 @default.
- W3094709780 hasConceptScore W3094709780C13280743 @default.