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
发布时间：2018-10-29 11:16
【摘要】：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。
[Abstract]:The effective identification of groundwater pollution source information is not only the basis of designing reasonable remediation scheme, but also the basis of clear responsibility and right to deal with pollution according to law. In this paper, the source inversion process is transformed into Bayesian inference process, and combined with the Kriging substitution model, a new idea for retrieving the history of groundwater pollution source release is proposed. At the same time, an improved Metropolis sampling algorithm is proposed. The results show that: (1) the method can effectively identify the release history of groundwater pollution sources, and the average relative error of the inversion results is 3.45; (2) under the condition of 500th iteration, the average relative error of the inversion result is reduced from 57.41% to 3.45% by the improved Metropolis algorithm, and the inversion efficiency and precision are improved effectively. (3) under the condition that the release rate of pollution source is different and disturbance exists, the inversion results are not deviated or fluctuated by a large margin, and the effect is stable.
【作者单位】： 吉林大学地下水资源与环境教育部重点实验室;吉林大学环境与资源学院;
【基金】：中国地调局项目(1212011140027,12120114027401) 吉林大学研究生创新基金项目(2015026)
【分类号】：X523
，
本文编号：2297541
[Abstract]:The effective identification of groundwater pollution source information is not only the basis of designing reasonable remediation scheme, but also the basis of clear responsibility and right to deal with pollution according to law. In this paper, the source inversion process is transformed into Bayesian inference process, and combined with the Kriging substitution model, a new idea for retrieving the history of groundwater pollution source release is proposed. At the same time, an improved Metropolis sampling algorithm is proposed. The results show that: (1) the method can effectively identify the release history of groundwater pollution sources, and the average relative error of the inversion results is 3.45; (2) under the condition of 500th iteration, the average relative error of the inversion result is reduced from 57.41% to 3.45% by the improved Metropolis algorithm, and the inversion efficiency and precision are improved effectively. (3) under the condition that the release rate of pollution source is different and disturbance exists, the inversion results are not deviated or fluctuated by a large margin, and the effect is stable.
【作者单位】： 吉林大学地下水资源与环境教育部重点实验室;吉林大学环境与资源学院;
【基金】：中国地调局项目(1212011140027,12120114027401) 吉林大学研究生创新基金项目(2015026)
【分类号】：X523
，
本文编号：2297541
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