基于贝叶斯推理与改进的MCMC方法反演地下水污染源释放历史

有效识别地下水污染源信息既是设计合理修复方案的基础,也是依法治污明确责权的依据。本文将污染源反演过程转化为贝叶斯推断过程,并与克里格替代模型相结合,提出了一种反演地下水污染源释放历史的新思路,同时针对求解过程中采用的Metropolis抽样算法提出改进方案。算例结果表明:(1)该方法能够有效识别地下水污染源释放历史,反演结果的平均相对误差为3.45%;(2)在500次迭代条件下,改进的Metropolis算法将反演结果的平均相对误差从57.41%降低至3.45%,有效提高了反演效率与精度;(3)在污染源释放速率有较大差异且存在扰动的条件下,反演结果并未出现大幅偏离或波动的异常,效果稳定。

Reconstructing the release history of groundwater contamination sources based on the Bayesian inference and improved MCMC method

Reconstructing the information of groundwater contamination sources effectively, is not only the foundation of designing a reasonable remediation project,but also the basis of governing pollution in accordance with the law and dividing the responsibility. In this paper,a promising approach was presented,according to which the recovering approach was considered as a Bayesian approach and combined with Kriging surrogate model. In addition,an improvement plan was proposed based on the Metropolis sampling algorithm. According to the results:(1) the new method can recover the release history of groundwater contaminant sources efficiently, whose results'' average relative error is 3.45%;(2) the improved Metropolis algorithm enhances the efficiency and accuracy of the inversion results obviously,which can decrease the average relative error from 57.41% to 3.45%, with the condition of 500 iterations;(3) the final results are stable,while the disturbance and difference between magnitude during different periods exist.