Full waveform inversion algorithm for subsurface physical property reconstruction using generative deep learning approach

Abstract

The inverse problem in seismic exploration is addressed by leveraging both the Radon transform and deep learning techniques to reconstruct subsurface properties from seismic data. The method encompasses a workflow that employs Generative Adversarial Networks (GANs) alongside specialized neural network architectures called R-nets. The first R-net efficiently handles hyperbolic Radon Transform, aiding in data preprocessing, while the second R-net generates detailed subsurface models. These reconstructed properties include the velocity of pressure wave propagation, velocity of shear wave propagation, impedance, and density. This novel approach significantly enhances the accuracy and efficiency of the reconstruction process, particularly beneficial for pre-stack depth migration. The use of both Radon transform and generative deep learning greatly reduces the time and resources traditionally needed for depth-velocity model construction, leading to the generation of superior subsurface models faster.