水力傳導係數(K)在地下水資源管理，和地下水汙染整治工作中扮演著重要的角色，然而K在空間上具有強烈的異質性。在資源有限的情況下，藉由抽水試驗及微水試驗只能得到少數的K值採樣。使用少量K值得到的推估K場，無法忠實呈現K場在空間上的異質性，導致地下水模擬結果具無法排除的不確定。而我們可以藉由豐富的鑽探岩相資料，做為K值大小的判示指標。雖然相對於抽水試驗和微水試驗這些資料所代表的K值具有較大的不確定性，但岩相資料具有量多且取得成本相對低廉的優勢。本研究以系集卡門濾波器單整合軟性資料以降低模擬之不確定性。研究中於二維地下水侷限含水層中進行數值試驗，使用水位資料推估K場做為對照組，並設計三個系列的案例測試系集卡門濾波器對K場推估的成效。在第一個系列的案例中，使用水位資料和不同精度及準度的K值觀測資料，測試具不確定性的K值觀測資料對K場推估的成效。在第二個系列的案例中，以第一個系列的其中案例為基礎，測試固定整合K值的次數，對K場推估的影響。在第三個系列的案例中，使用水位和岩相資料，測試岩相資料對K場推估的成效。這三個系列的案例使用場址中所有的網格的水位及水力傳導係數之平均誤差平方和當評估誤差指標。研究結果顯示(1)相對於單獨使用觀測水位進行K場推估，整合觀測水位與一定程度精度和準度的觀測K值，會有更好的K場推估結果。(2)並非整合精度和準度較低的觀測K值就一定會有較差的K場推估結果，準度較低的K值資料可以藉由給予適當的精度，提升K場推估結果。(3)在系集卡門濾波器中K值觀測值做為靜態資料，整合次數的多寡對於結果影響極小。(4)在成本有限的情況下，可以利用成本較低廉且具有不確定性的軟性資料，幫助我們提升K場推估結果(5)使用系集卡門濾波器結合水位與岩相資料，考有效改善K場的推估結果的改善。

Hydraulic conductivity (K) is an important parameter for groundwater resource management and subsurface contaminant remediation. Natural subsurface environment is inherently heterogeneous with hydrogeological parameters varying spatially. Only, the sparse K measurements can be obtained with pumping test and slug test due to high sampling cost. It is difficult to construct the heterogeneous K field with sparse K measurements and, thus, there is inherent uncertainty in groundwater modeling result. On the other hand, there are abundant facies measurements available. The facies measurements are relatively cheaper than direct K measurements obtained by pumping test and slug test and can serve as index of the magnitude of K with uncertainty. In this research, ensemble Kalman filter (EnKF) are explored for the reduction of modeling uncertainty. The ensemble Kalman filter is a data assimilation algorithm to integrate facies, K measurements and hydraulic head measurements for K estimate. A two-dimensional heterogenous confined aquifer is constructed for numerical modeling. Three series of cases are designed to evaluate the efficiency of lnK field estimations, using ensemble Kalman filter. In the first series, the sensitivity of accuracy and precision for lnK measurements in EnKF is tested. In the second series, the sensitivity of assimilation schedule for lnK measurements in EnKF is tested. In the third series, the feasibility of facies in EnKF is tested. The root mean square error (RMSE) of lnK evaluate the performance of EnKF. The results show: (1) Integrating h and lnK measurements to estimate lnK field through EnKF outperforms the integration of the h measurements only (2) The quality of the lnK measurement controls the performance of lnK field estimates. The more accurate and precise the lnK measurement is, the better the performance is. By given the proper precision of inaccurate lnK measurements, the performance can be significantly improved. (3) Assimilation schedule for lnK measurements makes little difference in the result of EnKF. It is proper to assimilate the lnK measurements just in the first time step only. (4) The state data such as hydraulic head can improve the performance of lnK estimates through EnKF. (5) Faces measurement such as soft data is feasible to improve the lnK estimates through EnKF.

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