论文详情
针对确定性叠前三参数反演存在的精度低、稳定性差、抗噪性差以及过度依赖初始模型的问题,虽然利用随机反演方法实现叠前三参数同步反演有助于解决这些问题,但此类方法反演效率较低。为此,提出了利用自适应粒子群优化马尔科夫链蒙特卡洛(APSO-MCMC)算法以提高叠前三参数反演的计算效率。结合纵波速度、横波速度以及密度的变化统计扰动关系,建立模型扰动的高斯分布,根据三参数扰动先验分布抽样获取初始粒子群。在每一步迭代过程中,利用跃迁矩阵改变粒子的演化,根据粒子之间的距离计算演化因子,判定粒子群的收敛状态。此外,利用精英策略,帮助粒子实现跳出局部极小值的困境。由于传统叠前近似公式在宽角度区域与非近似公式误差较大,故利用Zoeppritz公式来保证叠前三参数反演在各个角度的精度。NS地区二维海洋实际数据应用表明,该方法估算纵波速度、横波速度和密度有效,稳定收敛,计算效率高于传统的随机方法,具有较好的精度和抗噪性。
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.
国家重点研发计划(2017YFC0602804-02)资助。