Stochastically simultaneous inversion of prestack data using APSO-MCMC method

Deterministic inversion of prestack data has problems concerning inaccuracy, instability, poor noise resistance, and excessive dependence on the initial model.Therefore, a stochastic inversion method was previously proposed to determine the prestack three-parameter simultaneous inversion; however, the efficiency of conventional stochastic inversion is low.In this study, the APSO-MCMC method was developed to improve the computational efficiency of the prestack simultaneous inversion.According to the statistical perturbation relationship of the P-wave velocity, S-wave velocity, and density, the Gaussian distribution of the model perturbation is constructed.Based on the acceptance-rejection method, an initial swarm is generated as an input for the inversion process.During the iteration, a transition matrix is employed to change the evolution of the particles.Additionally, the evolution factor was calculated based on the distance between the particles to determine the convergence state of the particle swarm.In each iteration, an elite learning strategy was employed to determine the jump-out of the local minimum.Because the conventional forward modeling algorithm is not accurate in wide-angle regions, this study employed the Zoeppritz equations to ensure the accuracy of the prestack three-parameter inversion.The field data test of the NS area shows that the method correctly estimates the P-wave velocity, S-wave velocity, and density, and has a stability of convergence, high resistance to noise, and better efficiency than conventional stochastic approaches.