Assimilation of hydrogeophysical data for the characterization of subsurface heterogeneity using Ensemble Kalman Filter (EnKF)
KANG Xueyuan1, SHI Xiaoqing1, DENG Yaping1, LIAO Kaihua2, WU Jichun1
1. Key Laboratory of Surficial Geochemistry, School of Earth Science and Engineering, Nanjing University, Nanjing 210023, China; 2. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Abstract：Characterization of spatial variability of hydrogeologic properties is the key to simulate and predict the fate and transport of contaminants in the subsurface. In this study, we present a sequential data assimilation framework to estimate the heterogeneous saturated hydraulic conductivity fields through the assimilation of Electrical Resistivity Tomography (ERT)-monitored data and groundwater flow/transport observation data. This framework is integrated Ensemble Kalman Filter (EnKF), groundwater flow/transport models and effective medium resistivity model. To test the performance of the framework, synthetic cases of contaminant transport are reconstructed. We compare the performance of the coupled and uncoupled methods. The factors to control the performance of coupled and uncoupled methods are also discussed in a number of different scenarios. Results showed that both methods can effectively estimate the spatial distribution of hydraulic conductivity via time-lapse ERT-monitored data. The coupled method performs better than the uncoupled one when the prior statistics are close to real field. Meanwhile, the uncoupled method is more robust when the prior statistics is biased. The accuracy of estimated heterogeneous parameter field could be improved when integrating of multiple type observations including ERT-monitored data and a few observations of groundwater flow/transport model (i. e., concentration). As the uncoupled method requires a small computational effort compared to the coupled one, it is suggested to use the uncoupled method as a preliminary inversion before refining the results with a fully coupled method. We conclude that integrating multiple types of observations is recommended to improve the ability to delineate subsurface heterogeneity.
康学远, 施小清, 邓亚平, 廖凯华, 吴吉春. 基于EnKF融合地球物理数据刻画含水层非均质性[J]. 水科学进展, 2018, 29(1): 40-49.
KANG Xueyuan, SHI Xiaoqing, DENG Yaping, LIAO Kaihua, WU Jichun. Assimilation of hydrogeophysical data for the characterization of subsurface heterogeneity using Ensemble Kalman Filter (EnKF). Advances in Water Science, 2018, 29(1): 40-49.
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2018, Vol. 29 
