Deep neural network reducing numerical dispersion for post-processing of seismic modeling results

Author:

Gadylshina K. A.1,Lisitsa V. V.1ORCID,Vishnevsky D. M.1,Gadylshin K. G.1

Affiliation:

1. Trofimuk Institute of Petroleum Geology and Geophysics SB RAS

Abstract

The article describes a new approach to seismic modeling that combines calculations using traditional finite difference methods with the deep learning tools. Seismograms for the training data set are calculated using a finite difference scheme with high-quality spatial and temporal discretization. A numerical dispersion mitigation neural network is trained on the training dataset and applied to inaccurate seismograms calculated on a raw grid with a large spatial spacing. The paper presents a demonstration of this approach for 2D model; it is showing a tenfold acceleration of seismic modeling.

Publisher

Trofimuk Institute of Petroleum Geology and Geophysics (SB RAS)

Subject

General Medicine

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1. Development and application of neural network technology in solving geodynamic problems;Journal «Izvestiya vuzov Investitsiyi Stroyitelstvo Nedvizhimost»;2023-10-17

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