Work

Deep Learning-based Numerical Dispersion Mitigiation in Seismic Modelling

Year: 2021

Type: article

Abstract: Seismic modelling is the most computationally intense and time consuming part of seismic processing and imaging algorithms. Indeed, generation of a typical seismic data-set requires approximately 10 core-hours of a standard CPU-based clusters. Such a high demand in the resources is due to the use of fine spatial discretizations to achieve a low level of numerical dispersion (numerical error). This paper presents an original approach to seismic modelling where the wavefields for all sources (right-hand sides) are simulated inaccurately using coarse meshes. A small number of the wavefields are generated with computationally intense fine-meshes and then used as a training dataset for the Deep Learning algorithm - Numerical Dispersion Mitigation network (NDM-net). Being trained, the NDM-net is applied to suppress the numerical dispersion of the entire seismic dataset.

Source: Interexpo GEO-Siberia

DOI: 10.33764/2618-981x-2021-2-2-17-25

Authors: Kseniia A. Gadylshina, Kirill G. Gadylshin, Vadim V. Lisitsa, Dmitry M. Vishnevsky

Institutions: Institute of Petroleum Geology and Geophysics, Sobolev Institute of Mathematics

Language: English

Topic: Seismic Imaging and Inversion Techniques

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W3202139095

FWCI:

Cites: 16

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Related to: 10