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

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.