尾矿材料渗透系数序贯概率反演分析

为保证尾矿库坝体渗流分析的准确性,推断尾矿材料渗透系数概率分布并减少其不确定性,提出基于贝叶斯更新的尾矿材料参数序贯概率反演方法,建立尾矿坝水位代理模型及似然函数,并以大黑山尾矿坝为工程背景,利用不同时刻从不同监测点测得的尾矿坝水位值,对多层尾矿材料的渗透系数进行序贯概率反演分析。结果表明:提出的基于贝叶斯更新的序贯概率反演方法能够有效推断大黑山尾矿材料渗透系数及概率分布,降低渗透系数变异系数,与水位监测点位置距离较近土层渗透系数的变异系数缩减了18.25%;单独利用尾矿坝水位监测值不能准确推断尾矿材料渗透系数及表征其真实不确定性,需要进一步收集多源监测信息并融合到参数概率反演分析中。

Sequential probabilistic back analysis on hydraulic conductivity of tailings materials

In order to ensure seepage analysis accuracy of tailings dam,deduce hydraulic conductivity probability distribution of tailings material and to reduce its uncertainty,sequential probabilistic back analysis method of material parameters based on Bayesian updating was proposed.Then,a surrogate model of water table and likelihood function were constructed.Finally,with Daheishan tailings dam taken as an example,sequential probabilistic back analysis of hydraulic conductivity of multi-layered tailings materials was conducted based on monitoring data of water tables.The results show that the proposed approach can effectively infer hydraulic conductivity and probability distributions as well as reduce their variation coefficients which is reduced by 18.25%for soil layer closer to monitoring points.Realistic uncertainties of hydraulic conductivity and representation cannot be well deduced only from monitoring information of water levels,and it is necessary to further collect field information of multiple sources and incorporate it into probabilistic back analysis.