The ensemble Kalman filter (EnKF) was used to estimate the spatial distribution of the Young's modulus of a model of an earth-fill dam by assimilating the travel time to the first arrival of the surface waves. By the ensemble data assimilation, the measured data from a geophysical exploration was applied to simultaneously estimate the geotechnical properties and evaluate the uncertainties. Swedish weight sounding (SWS) test results were employed as the prior information to generate the initial ensemble through the sequential Gaussian simulation (sGs). In the experiments of assimilation, it was shown that the reproducibility of the parameter field is enhanced by this initial ensemble generation method, and that the uncertainties of the identified parameters can be reduced by the assimilation.

中文翻译：

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基于随机场建模的集合卡尔曼滤波器的表面波方法数据同化

集合卡尔曼滤波器 (EnKF) 用于通过将传播时间同化到表面波的首次到达来估计填土坝模型的杨氏模量的空间分布。通过集合数据同化，将来自地球物理勘探的测量数据应用于同时估计岩土特性和评估不确定性。瑞典重量探测 (SWS) 测试结果被用作先验信息，通过顺序高斯模拟 (sGs) 生成初始集合。同化实验表明，这种初始集合生成方法提高了参数场的再现性，同化可以降低识别参数的不确定性。