A New Stochastic Process of Prestack Inversion for Rock Property Estimation

• Published in: Applied sciences Vol. 12; no. 5; p. 2392
• Main Authors: Yin, Long, Zhang, Sheng, Xiang, Kun, Ma, Yongqiang, Ji, Yongzhen, Chen, Ke, Zheng, Dongyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2022
• Subjects: adaptive particle swarm optimization; Algorithms; Bayesian analysis; Data logging; Efficiency; Elitism; Global optimization; Inverse problems; Inversion; Markov Chain Monte Carlo; Markov chains; Methods; Optimization; Parameter estimation; prestack stochastic inversion; Statistical analysis; Stochastic models; Stochastic processes; Stochasticity; the global optimal value; Velocity; Workflow
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12052392

In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas.