DOI: https://doi.org/10.26089/NumMet.v24r215

Training data set construction based on the Hausdorff metric for numerical dispersion mitigation neural network in seismic modelling

Authors

  • Kseniia A. Gadylshina
  • Dmitry M. Vishnevsky
  • Kirill G. Gadylshin
  • Vadim V. Lisitsa

Keywords:

seismograms numerical modelling
numerical dispersion
deep learning
teaching dataset creation

Abstract

The article outlines a strategy for constructing a training data set for a numerical dispersion mitigation network (NDM-net), consisting in the calculation of the full set of seismograms by the finite difference method on a coarse grid and the calculation of the training sample using a fine grid. The training dataset is a small set of seismograms with a certain spatial distribution of wave field sources. After training, the NDM-net allows approximating low-quality coarse-grid seismograms into seismograms with a smaller sampling step. Optimization of the process of constructing a representative training dataset of seismograms is based on minimizing the Hausdorff metric between the training sample and the full set of seismograms. The use of the NDM-net makes it possible to reduce time costs when calculating wave fields on a fine grid.

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Published

2023-05-15

Issue

Vol. 24 (2023): Issue 2.

Section

Methods and algorithms of computational mathematics and their applications

Author Biographies

Kseniia A. Gadylshina

Trofimuk Institute of Petroleum Geology and Geophysics of SB RAS
• Junior Researcher

Dmitry M. Vishnevsky

Trofimuk Institute of Petroleum Geology and Geophysics of SB RAS
• Researcher

Kirill G. Gadylshin

LLC RN-BashNIPIneft
• Expert

Vadim V. Lisitsa

Trofimuk Institute of Petroleum Geology and Geophysics of SB RAS
• Head of Laboratory

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