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| 研究生： |
陳尚潁 Chen, Shang-Ying |
|---|---|
| 論文名稱： |
條件化水文地質資料對地下水流不確定性減量之研究 Uncertainty Reduction of Subsurface Flow by Conditioning Hydrogeological Data |
| 指導教授： |
徐國錦
Hsu, Kuo-Chin |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工學院 - 資源工程學系 Department of Resources Engineering |
| 論文出版年： | 2017 |
| 畢業學年度： | 105 |
| 語文別： | 英文 |
| 論文頁數： | 57 |
| 中文關鍵詞： | 序率式 、條件化 、邊界型 、不確定性量化 、動差偏微分方程式 、無網格法 、廣義有限差分法 |
| 外文關鍵詞： | Stochastic, Conditioning, Bounded, Uncertainty Quantification, Moment Differential Equation, Meshless Method, Generalized Finite Difference Method |
| 相關次數： | 點閱：118 下載：21 |
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由於地質材料的異質性及現地資料的稀缺性，水文地質模式具有不確定性。本研究應用基於微小擾動法的統計動差偏微分方程式計算預測的不確定性。研究中使用水文地質資料如水力傳導係數、水頭或岩相資料，進行個別或同時條件化。本研究採用新發展的無網格法－廣義有限差分法，利用其可任意分佈計算點的特性，計算統計第一動差－平均值及第二動差－變異數。條件化資料隨機採樣自逐步高斯模擬法所產生一空間相關的假想隨機真實場。本研究量化出不同類型的量測資料對不確定性減量的效果，結果顯示同時條件化多種資料能改良水頭估測、有效降低不確定性。
Because of the heterogeneity of geological materials and scarcity of in-situ data, the geological model is usually uncertainty embedded. In this study, the statistical moment differential equation (ME) based on the small perturbation method is applied to assess predictive uncertainty. The models were conditional on geological data such as hydraulic conductivity, hydraulic head or/and lithofacies jointly or separately. The meshless generalized finite difference method (GFDM) is adopted to obtain the first and second moment solutions advantageously and conveniently by virtue of its arbitrarily-distributed computational nodes. The conditioning data were randomly sampled from a hypothetical field with spatially correlated data, which was generated by Sequential Gaussian simulation (SGSIM). This study quantifies how different types of measurements act jointly or separately to reduce the predictive uncertainty of conditional models. The results show that, conditioning different types of measurements yields improved estimates of head.
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校內：2022-07-01公開校外：2022-07-01公開