推估異質性水力傳導係數在地下水資源管理與地下污染整治上十分重要，但是直接觀測的水力傳導係數資料十分稀少，因此推估結果存在不確定性。本研究使用貝氏統計方法同化次要資料以降低推估不確定性。不同於傳統地質統計方法，貝氏統計方法可以考慮資料的非線性關係。本研究利用假設案例驗證此方法的可行性。結果顯示，當主要資料與次要資料間存在高相關性，增加次要資料的數量可以有效率的改善水力傳導係數的估計結果。然而，資料的型態對於估計結果的影響不大。另外，本研究利用此方法探討電阻率測錄和岩性紀錄在推估濁水溪沖積扇水力傳導係數分布的有效性。結果顯示，同時使用兩種次要資料可以得到最精準的估測結果。相對於普通克利金的結果，含水層一不確定性下降達37.3 %，含水層二不確定性下降達9.6 %。此外，含水層一的水力傳導係數推估結果與先前研究推估的岩相分布具有相似的空間分布。

Characterizing spatially heterogeneous hydraulic conductivity (K) plays a crucial role in groundwater resources management and subsurface contaminant remediation. Since the direct measurements of K are sparse, the uncertainty is inherent in the estimated parameters. We propose to decrease the uncertainty by assimilating secondary data (related with K to some degree) with the primary data (K) using Bayesian statistical method. Different from classical geostatistical methods, both linear and nonlinear relations between the primary and secondary data can be considered in Bayesian statistical method. A synthetic example is designed to evaluate the method. Results show that increasing numbers of secondary data improves K estimates efficiently if higher correlation exists for primary and secondary data. However, the relation type has little influence on the accuracy and uncertainty. Also, we explore the use of resistivity logs and lithologic description for K estimation at Choushui River alluvial fan using the method. Results indicate jointly assimilating both types of secondary data can obtain the most accurate and least uncertain estimates. By combining both types of data with K data, a less uncertain K distribution of Choushui River alluvial fan is obtained. The K distribution of aquifer 1 has similar spatial variation with the result of facies distribution from previous study. The reduction efficiency of uncertainty is 37.3 % for aquifer 1 and 9.6 % for aquifer 2 compared to the uncertainty by ordinary kriging.

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