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dc.contributor.authorChen, Yuqing
dc.contributor.authorSaygin, Erdinc
dc.contributor.authorSchuster, Gerard T.
dc.date.accessioned2020-10-06T13:54:42Z
dc.date.available2020-10-06T13:54:42Z
dc.date.issued2020-09-30
dc.identifier.citationChen, Y., Saygin, E., & Schuster, G. T. (2020). Seismic inversion by multi-dimensional Newtonian machine learning. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425975.1
dc.identifier.doi10.1190/segam2020-3425975.1
dc.identifier.urihttp://hdl.handle.net/10754/665468
dc.description.abstractNewtonian machine learning (NML) inversion has been shown to accurately recover the low-to-intermediate wavenumber information of subsurface velocity models. This method uses the wave-equation inversion kernel to invert the skeletonized data that is automatically learned by an autoencoder. The skeletonised data is a one-dimensional latent-space representation of the seismic trace. However, for a complicated dataset, the decoded waveform could lose some details if the latent space dimension is set to one, which leads to a low-resolution NML tomogram. To mitigate this problem, an autoencoder with a higher dimensional latent space is needed to encode and decode the seismic data. In this paper, we present a wave equation inversion that inverts the multi-dimensional latent variables of an autoencoder for the subsurface velocity model. The multi-variable implicit function theorem is used to determine the perturbation of the multi-dimensional skeletonised data with respect to the velocity perturbations. In this case, each dimension of the latent variable is characterized one gradient and the velocity model is updated by the weighted sum of all these gradients. Numerical results suggest that the multidimensional NML inverted result can achieve a higher resolution in the tomogram compared to the conventional single dimensional NML inversion.
dc.description.sponsorshipWe thanks to the Deep Earth Imaging Future Science Platformof CSIRO for funding and computing resources of CSIRO.
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/segam2020-3425975.1
dc.rightsArchived with thanks to Society of Exploration Geophysicists
dc.titleSeismic inversion by multi-dimensional Newtonian machine learning
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.eprint.versionPost-print
dc.contributor.institutionDeep Earth Imaging Future Science Platform, CSIRO
kaust.personSchuster, Gerard T.
refterms.dateFOA2020-10-07T09:23:54Z
dc.date.published-online2020-09-30
dc.date.published-print2020-09-30


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