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dc.contributor.authorPuzyrev, Vladimir
dc.contributor.authorEgorov, Anton
dc.contributor.authorPirogova, Anastasia
dc.contributor.authorElders, Christopher
dc.contributor.authorOtto, Claus
dc.identifier.citationPuzyrev, V. and Egorov, A. and Pirogova, A. and Elders, C. and Otto, C. 2019. Seismic Inversion with Deep Neural Networks: a Feasibility Analysis. In: 81st EAGE Conference and Exhibition 2019, 3rd Jun 2019, London, UK.

We investigate deep learning approaches to inversion of a 1D model of the subsurface using synthetic surface seismic and VSP data. Several deep neural networks based on three different architectures are developed and tested. The matrix propagator technique is used to generate the synthetic data for network training. The pre-trained deep neural networks can instantly predict velocity models from new data in a single step. The synthetic datasets used in training can be extended by adding random noise to the existing data, thus making the method closer to real-world conditions.

dc.titleSeismic Inversion with Deep Neural Networks: a Feasibility Analysis
dc.typeConference Paper
dcterms.source.conference81st EAGE Conference and Exhibition 2019
dcterms.source.conference-start-date3 Jun 2019
dcterms.source.conferencelocationLondon, UK
curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidPuzyrev, Vladimir [0000-0002-0264-6126]
dcterms.source.conference-end-date6 Jun 2019
curtin.contributor.scopusauthoridPuzyrev, Vladimir [36020700900]

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