The characterization of in situ ground conditions is essential for geotechnical practice. The probabilistic estimation of soil parameters can be achieved via updating with monitoring data within the Bayesian framework. The estimation of spatially varying soil parameters is seldom undertaken with time-variant monitoring data. In this study, an efficient Bayesian method is presented for the estimation of spatially varied saturated hydraulic conductivity k_s of unsaturated soil slope with spatiotemporal monitoring data. The computationally cheap surrogate model of the adaptive sparse polynomial chaos expansion method is adopted to approximate the transient numerical model. Markov chain Monte Carlo method is used for the probabilistic estimation of basic random variables. Based on the hypothetical cases, the effects of monitoring frequency and stage are studied. The errors and the uncertainties of the estimated k_s fields are increased with the decreasing monitoring frequency. Bayesian estimation of spatial variability is more accurate when using the later stage of monitoring data. The estimated method is further verified with a real case study by the comparison of borehole data, dynamic probe test (DPT) data, and field monitoring data. The distribution of the soil types acquired from boreholes is reflected in the estimated k_s. The estimated field of k_s has a certain agreement with the borehole log and DPT measurements and can reproduce the spatial variability of the site to an acceptable degree.

现场地面条件的表征对于岩土工程实践至关重要。通过在贝叶斯框架内更新监测数据可以实现土壤参数的概率估计。很少使用时变监测数据来估算空间变化的土壤参数。在这项研究中，提出了一种有效的贝叶斯方法来估算空间变化的饱和导水率k _s时空监测数据分析非饱和土边坡 采用自适应稀疏多项式混沌展开方法的廉价计算替代模型来近似瞬态数值模型。马尔可夫链蒙特卡罗方法用于基本随机变量的概率估计。基于假设的情况，研究了监测频率和阶段的影响。估计k _s的误差和不确定性场随着监视频率的降低而增加。使用监视数据的后期阶段时，对空间变异性的贝叶斯估计更准确。通过比较井眼数据，动态探针测试（DPT）数据和现场监测数据，通过实际案例进一步验证了该估计方法。从钻孔中获取的土壤类型的分布反映在估计的k _s中。k _s的估计场与井眼测井和DPT测量值具有一定的一致性，并且可以将站点的空间变异性再现到可接受的程度。