【摘要】 本文介绍了用MCMC(马尔可夫链蒙特卡洛)方法估计地震参数的基本原理及应用。首先利用MCMC方法生成马尔可夫链,然后对链进行统计分析,得到未知参数的估计值。在运用Metropolis-Hastings算法生成马尔可夫链时,可随着迭代次数的增加逐渐减小其游走的步长,以确保迭代初期较早收敛到真值附近,迭代后期在真值附近能得到精度较高的估计值。模型试算结果表明:反演结果与理论模型基本吻合,实际资料的应用效果也证明了算法的有效性。更多还原

【Abstract】 This paper introduces the basic theory and applications of Markov Chain Monte Carlo(MCMC) method for estimating seismic parameters.By making statistical analysis of the chains which are generated by MCMC,the estimates of unknown parameters are obtained.The algorithm for chain generation is Metropolis-Hastings algorithm,and the step length in proposal distribution function decreases as the number of iterations goes up,which ensure the speed to converge at the beginning and the precision of the estimation.The application of both synthetic data and real data shows the MCMC method for elastic parameter estimation is feasibly and effective.更多还原