Inpainting of local wavefront attributes using artificial intelligence

Kirill Gadylshin, Ilya Silvestrov, Andrey Bakulin

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

We propose a fast method to calculate local wavefront attributes for 3D prestack seismic data. First step is to compute attributes on a coarse regular or irregular grid in time and space using conventional approaches. Second step is very fast and efficient inpainting of the attributes in remaining locations by artificial intelligence utilizing a specially trained deep neural network. The method incorporates multi-parameter attributes using a special colouring scheme and allows estimation of multiple attributes simultaneously during one run. We demonstrate that inpainting of local wavefront attributes for nonlinear beamforming can greatly speed up prestack enhancement of 3D seismic data. Other applications such as velocity analysis or seismic tomography can be implemented using a similar approach.

Original languageEnglish
Pages2212-2216
Number of pages5
DOIs
Publication statusPublished - 2020
EventSociety of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019 - San Antonio, United States
Duration: 15 Sep 201920 Sep 2019

Conference

ConferenceSociety of Exploration Geophysicists International Exposition and Annual Meeting 2019, SEG 2019
CountryUnited States
CitySan Antonio
Period15.09.201920.09.2019

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