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




seismograms numerical modelling, numerical dispersion, deep learning, teaching dataset creation


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.

Author Biographies

Kseniia A. Gadylshina

Dmitry M. Vishnevsky

Kirill G. Gadylshin

• Expert

Vadim V. Lisitsa


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How to Cite

Гадыльшина К.А., Вишневский Д.М., Гадыльшин К.Г., Лисица В.В. Training Data Set Construction Based on the Hausdorff Metric for Numerical Dispersion Mitigation Neural Network in Seismic Modelling // Numerical Methods and Programming (Vychislitel’nye Metody i Programmirovanie). 2023. 24. 195-212. doi 10.26089/NumMet.v24r215



Methods and algorithms of computational mathematics and their applications