A new approach to remove the numerical dispersion in elastic wave modeling using R-Cycle-GAN networks

Abstract

Abstract The finite difference forward modeling has been widely used in geophysics exploration and petroleum fields. Because of its high efficiency and easy application for graphical processing units, it has been widely concerned by industry and academia. However, owing to many factors, the problem of numerical dispersion has been an important factor hindering this method. To overcome the numerical dispersion, this paper proposes a method for removing numerical dispersion using deep learning. Unlike the conventional optimized algorithms target to optimize the finite difference coefficients, our strategy is based on big data training to eliminate the dispersion data after seismic data modeling. We design a neural network architecture based on cycle-consistent generative adversarial networks (Cycle-GANs) and residual learning for elastic wave propagation. Under the premise of not significantly increasing the calculation time, we can obtain higher calculation accuracy. Compared with the high-order finite difference algorithm, the calculation time is the advantage of our proposed deep learning method. Tests prove the efficiency and stability of our proposed algorithm.