整合地理統計及資料同化方法於水文地質架構推估與地下水模型建立-以濁水溪沖積扇為例

Abstract

在環境科學與工程應用等領域，受限於操作條件、經費或自然干擾，常面臨觀測資料不足及空間解析度不足的問題，對區域特性研究造成挑戰。濁水溪沖積扇作為台灣重要的農業重鎮，具有複雜的地質條件與地下水分布，且頻繁出現地層下陷與鹹水入侵等問題，對該區域地下水資源的管理與評估提出了更高的要求。因此，掌握其水文地質架構並建立可靠的地下水模型成為緩解地層下陷及管理的基礎。 本研究旨在推估濁水溪沖積扇的三維水文地質架構及分層水力傳導係數場，並進一步以資料同化精進模擬地下水流動的模型。首先，基於有限的水文地質剖面資料、岩性鑽探資料及三維電阻率模型等多種資料，採用eXtreme Gradient Boosting(XGBoost)推估濁水溪沖積扇的三維岩性機率場，作為後續建模的不確定性資料。接著，透過類別型貝氏最大熵法(Categorical BME)，結合以上資料建立完整的三維岩性推估模型，得到細緻的三維岩性場，並且進一步以核密度估計與資料科學方法為地下水模型進行精確分層。 隨後，為了進行分層水力傳導係數的推估，本研究應用連續型貝氏最大熵法(Continuous BME)，結合地理加權迴歸(Geographically Weighted Regression, GWR)，利用岩性及地球物理數據建立含水層的水力傳導係數場模型。在建立地下水模型階段，本研究採使用MODFLOW模擬地下水流動過程，並運用集合平滑器(Ensemble Smoother, ES)進行參數資料同化。此方法通過動態更新參數(如抽水補注量及水力傳導係數)，有效整合觀測數據與模型結果，顯著提升模擬精度，同時減少傳統參數率定過程的時間成本。本研究不僅解決了區域地質結構的推估難題，亦探索了以自動化方式結合多源資料與物理模型的潛力，期望能為地層下陷的防治及地下水資源的永續管理提供參考與支持。

In the fields of environmental science and engineering applications, the lack of observational data and insufficient spatial resolution, often constrained by operational conditions, budget limitations, or natural interferences, poses challenges to regional characteristic studies. The Choshui River alluvial fan, a critical agricultural region in Taiwan, exhibits complex geological conditions and groundwater distribution. It frequently experiences land subsidence and seawater intrusion, raising the demands for effective management and assessment of groundwater resources. Therefore, understanding its hydrogeological framework and establishing a reliable groundwater model forms the foundation for mitigating land subsidence and optimizing resource management. This study aims to estimate the three-dimensional hydrogeological framework and stratified hydraulic conductivity field of the Choshui River alluvial fan, further enhancing the simulation of groundwater flow through data assimilation. First, using limited hydrogeological profile data, lithological drilling data, and a three-dimensional resistivity model, eXtreme Gradient Boosting (XGBoost) was applied to estimate the three-dimensional lithological probability field of the Choshui River alluvial fan, providing soft data for subsequent modeling. Subsequently, the categorical Bayesian Maximum Entropy (Categorical BME) method was employed to integrate the above data and establish a comprehensive three-dimensional lithological estimation model, resulting in a detailed three-dimensional lithological field. Furthermore, kernel density estimation and data science methods were applied to achieve accurate stratification for the groundwater model. Next, to estimate the stratified hydraulic conductivity, the study applied the continuous Bayesian Maximum Entropy (Continuous BME) method, combined with Geographically Weighted Regression (GWR), to construct a hydraulic conductivity field model for aquifers using lithological and geophysical data. During the groundwater modeling phase, MODFLOW was utilized to simulate groundwater flow processes, and the Ensemble Smoother (ES) was employed for parameter data assimilation. This approach dynamically updated parameters (e.g., pump discharge, recharge and hydraulic conductivity), effectively integrating observational data with model results, significantly enhancing simulation accuracy while reducing the computational cost of traditional parameter calibration processes. This study not only addresses the challenges in estimating regional geological structures but also explores the potential of integrating multi-source data with physical models using automated approaches. The findings are expected to contribute to the prevention of land subsidence and the sustainable management of groundwater resources.