Stochastic seismic acoustic impedance inversion via a Markov-chain Monte Carlo method using a single GPU card

Abstract

Seismic acoustic impedance inversion plays an important role in understanding subsurface structures and obtaining subsurface properties. The stochastic approach is one of the methods used for impedance inversion, and it aims to produce more reliable results by accounting for modeling uncertainty. Stochastic inversion represents the uncertainty of a subsurface model as a probability distribution and uses this distribution to estimate model parameters. In this study, seismic acoustic impedance inversion was performed using a Markov-chain Monte Carlo approach, which is a sampling method used in the stochastic process. Utilizing Bayesian inference based on prior information and observed data, we implemented acceptance probabilities and performed acoustic impedance inversion for field data through iterative calculations. We employed a single GPU card to execute the inversion algorithm and conducted a comparative analysis with the results obtained using a cluster composed of multiple CPU cores. Through a computational speed analysis, the efficiency of the algorithm using a GPU was verified, while uncertainty analysis was employed for algorithm validation. Through these analyses, we confirmed the feasibility of applying our developed GPU algorithm to tasks that require the inversion of extensive data, such as high-resolution seismic exploration and CO2 storage monitoring.