Abstract
This study utilizes GPU computing to perform seismic full waveform inversion, a high-dimensional and ill-posed problem. By employing hybrid Hamiltonian Monte Carlo methods, it enables efficient computation of the posterior distribution under various priors, which regularize the problem. The approach facilitates assessing the efficiency of different priors and regularization techniques. As high-performance computing continues to advance, these methods allow for the development of more sophisticated inversion algorithms for large-scale seismic problems, improving uncertainty estimation and aiding decision-making.
