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整治場址數位監測與高精度污染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2)

title: 整治場址數位監測與高精度污染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2) abstract: 本計畫以佶鼎科技股份有限公司廠址作為模場試驗場域，驗證與優化土壤及地下水污染整治技術。調查結果顯示，地下水中銅(Cu)與鎳(Ni)污染分布面積分別為507 及 811.2 平方公尺，另有多口監測井檢出鉛(Pb)濃度超標，顯示場址具多重重金屬污染特性。主要採用抽出處理法(Pump and Treat)控制污染，雖整體濃度呈下降趨勢，仍有局部監測井超標。為提升整治效能，輔以現地土壤淋洗法(In-situ SoilFlushing)，促進土壤中金屬脫附並抽取處理，並已完成模場試驗。場址含水層由多層砂質與黏土交互組成，具高度異質性， 7~8 公尺處之黏土層形成滯留屏障，使污染物分佈不均，抽出與灌注效率受限。此外，地下水流向受降雨與鄰近抽水行為影響，流場變異大，增加整治操作與成效評估之挑戰。因此，本模場計畫規劃共兩年研究期程，第一年提出技術需求與獲得研究成果，包括： (1)即時地下水位與基礎水質觀測以強化整治策略規劃； (2)評估含水層水力傳導係數空間分布以增進抽注作業效率； (3)運用光纖高解析監測技術評估垂直向導水特性及水流通量。為強化地質分層與材料分布之空間解析，提高藥劑注入與抽出過程效率。本年度將以第一年之觀測資料與試驗成果為基礎，導入長短期記憶網路(Long Short-Term Memory, LSTM)結合退火演算法(Simulated Annealing, SA)之深度學習架構，進行污染物濃度時空變化的預測與溯源分析。LSTM 模型能有效捕捉地下水污染傳輸中具非線性與時序依賴的特性，藉由大量歷史水位、水質與抽灌操作資料之訓練，預測未來污染濃度與趨勢；退火演算法則用於模型參數與初始權重之全域優化，以避免深度學習陷入局部極值，提升預測穩定度與泛化能力。此組合技術可同時進行「污染來源區反演」與「整治策略模擬」，透過比對模擬結果與監測資料差異，不斷迭代修正模型參數，最終建立污染源時空分布的最佳化反演結果。整體技術具備自動化訓練流程與高效參數搜尋能力，可有效提升多重污染(Cu、 Ni、 Pb)傳輸之預測精度，協助識別主要污染來源、評估整治成效，並提供後續決策支援。;This project designates the Gi Ding Technology Co., Ltd. industrial site as a pilot-scale test field to validate and optimize soil and groundwater contamination remediation technologies. Site investigations indicate that copper (Cu) and nickel (Ni) contamination in groundwater cover areas of approximately 507 m² and 811.2 m², respectively. In addition, multiple monitoring wells exhibit lead (Pb) concentrations exceeding regulatory standards, demonstrating the presence of multi-metal contamination at the site. Pump-and-treat remediation has been implemented as the primary control measure, and although overall contaminant concentrations show a declining trend, exceedances persist at several localized monitoring points. To enhance remediation efficiency, in-situ soil flushing has been employed as a supplementary technique to promote desorption of metals from the soil matrix followed by extraction and treatment of contaminated groundwater. Pilot-scale testing of this approach has been completed. The site aquifer system consists of interbedded sandy and clayey layers with pronounced heterogeneity. A clay layer at depths of approximately 7–8 m acts as a semi-confining barrier, resulting in uneven contaminant distribution and limiting the effectiveness of extraction and injection operations. Furthermore, groundwater flow directions are strongly influenced by precipitation events and nearby pumping activities, leading to highly variable flow fields and increased uncertainty in remediation operation and performance evaluation. Accordingly, the pilot study is structured over a two-year research period. In the first year, technical needs were identified and key outcomes were achieved, including: (1) implementation of real-time groundwater level and baseline water quality monitoring to strengthen remediation strategy development; (2)assessment of the spatial variability of aquifer hydraulic conductivity to improve the efficiency of pumping and injection operations; and (3) application of high-resolution fiber-optic sensing techniques to evaluate vertical hydraulic connectivity and groundwater fluxes. These efforts aim to refine the spatial resolution of stratigraphic layering and material distribution, thereby enhancing the effectiveness of chemical injection and extraction processes. Building upon the observational data and experimental results obtained in the first year, the second-year research will integrate a deep learning framework combining Long Short-Term Memory (LSTM) networks with a Simulated Annealing (SA) algorithm to predict the spatiotemporal evolution of contaminant concentrations and to conduct source identification analyses. LSTM models are well suited to capture the nonlinear behavior and temporal dependencies inherent in groundwater contaminant transport. Trained on extensive historical datasets of ground water levels, water quality measurements, and pumping–injection operations, the model will forecast future contaminant concentrations and trends. The simulated annealing algorithm will be employed for global optimization of model parameters and initial network weights, mitigating the risk of convergence to local minima and improving prediction robustness and generalization performance. This integrated approach enables simultaneous “contaminant source inversion” and “remediation strategy simulation.” By iteratively minimizing discrepancies between simulated outputs and observed monitoring data, model parameters are progressively refined to derive an optimized reconstruction of the spatiotemporal distribution of contamination sources. Overall, the proposed methodology features an automated training workflow and efficient parameter search capability, substantially enhancing predictive accuracy for multi-metal contaminant transport (Cu, Ni, Pb), supporting identification of primary contamination sources, evaluation of remediation effectiveness, and provision of robust decision support for subsequent site management.

裂縫網絡結構控制參數的表徵：基於滲流與裂縫長度的流道和誘發地震活動預測框架;Characterizing Fracture Network Structural Controls: a Predictive Framework for Flow Channeling and Induced Seismicity via Percolation and Fracture Length

title: 裂縫網絡結構控制參數的表徵：基於滲流與裂縫長度的流道和誘發地震活動預測框架;Characterizing Fracture Network Structural Controls: a Predictive Framework for Flow Channeling and Induced Seismicity via Percolation and Fracture Length abstract: 裂隙岩體中的地下水流動與溶質傳輸行為，是高階放射性廢棄物深地層處置、地熱能源開發及地質碳封存等國家重大工程之關鍵課題。然而，傳統離散裂隙網路（DFN）模擬多將裂隙視為靜態幾何，往往忽視了岩體在應力與水力耦合作用下，裂隙幾何型態（如長度、孔徑）的動態演化過程，以及岩石基質對整體流場的交互影響，導致滲透率與傳輸預測產生顯著偏差。為突破此限制，本計畫旨在開發一套創新的「混合域裂隙岩層水力-力學耦合數值模式」。研究核心整合岩石損傷演化理論與斷裂力學機制，並引入自適應非結構化網格細化技術。本研究將基於質量與動量守恆原理，建立考慮裂隙與基質交互作用之控制方程式，能量平衡理論模擬次臨界裂隙生長與活化行為，藉以精確捕捉裂隙在流體注入或地質應力改變過程中之動態邊界變化。在驗證與應用方面，計畫將透過合成案例分析，並與國際著名軟體進行交叉比對驗證。同時，將結合台灣離島結晶岩地區之現地調查參數，建立符合本土水文地質特性之模型。預期成果將產出一套具物理機制基礎之分析工具，能動態量化裂隙演化對水力參數之影響，為我國深地層處置安全評估與地質能源開發提供更具科學信賴度之決策依據。

探討台灣東北外海低重力異常成因;Investigation into the Causative Source of the Significant Gravity Anomaly Low Offshore Northeast Taiwan

title: 探討台灣東北外海低重力異常成因;Investigation into the Causative Source of the Significant Gravity Anomaly Low Offshore Northeast Taiwan abstract: 台灣位於兩個隱沒系統的交會處：東北部琉球隱沒帶，以及南部馬尼拉隱沒帶。在台灣東部及外海，菲律賓海板塊從覆蓋在歐亞板塊之上轉變為隱沒至歐亞板塊和琉球島弧之下，因此兩板塊間相互作用及其相關構造非常複雜。板塊間的相互作用產生了許多地震並在台灣陸地造成災害。對於台灣東部構造特性的了解及相關地震防災是很重要的工作。本計畫欲探討花蓮外海一個明顯而特殊的重力異常低區，其成因可能能為此構造複雜的區域提供ㄧ些想法。

結合 AI 聊書的多模態評量閱讀歷程動態分析;Multimodal Assessment Integrating Ai Book Conversations for Dynamic Analysis of Reading Processes

title: 結合 AI 聊書的多模態評量閱讀歷程動態分析;Multimodal Assessment Integrating Ai Book Conversations for Dynamic Analysis of Reading Processes abstract: 本計畫針對閱讀平台多偏重閱讀時數與單一文本紀錄，難以全面評估學生認知負荷與情意投入之缺口，提出一套結合「身教式持續安靜閱讀（MSSR）」、「興趣驅動創造者理論（IDC Theory）」與「多模態學習分析（MMLA）」之閱讀歷程分析。旨在深化前期「AI 聊書同伴」之基礎，核心目標為建置「多模態理解與分析模組」，將分析維度從文字擴展至全觀歷程，具體執行策略包含四大面向： 1. 視覺理解模組：運用電腦視覺技術辨識書籍封面與學生創作，將實體閱讀行為轉化為可運算之視覺特徵向量。此模組能解決傳統僅依賴關鍵字推薦之侷限，將視覺風格偏好納入演算法，提升互動之真實感與情境脈絡。 2. 語音理解模組：結合自動語音辨識與語音情感辨識，分析學生在朗讀與聊書時的流暢度、語調起伏與自信心。系統將自動標註朗讀錯誤與異常停頓，並偵測語音中隱含的情緒訊號，藉此評估其閱讀理解程度與情意投入狀態。 3. 行為特徵分析模組：透過時間戳記探勘閱讀頻率與規律性，診斷學生是否建立穩定之 MSSR 閱讀習慣。系統能識別異常閱讀行為（如刷閱讀量或放棄閱讀），並根據行為特徵將學生分群，作為 AI 介入策略調整之依據。 4. 多模態融合模組：透過深度學習之特徵嵌入與時序對齊技術，將上述視覺、語音與行為等異質數據交叉比對不同模態間的關聯，建構動態且立體的學習者模型。本計畫之預期成果，在於賦能「AI 聊書同伴」具備感知能力，使其能根據學生的即時多模態狀態，提供具備同理心之對話引導與適性化鷹架。同時，系統將產出自動化之多模態閱讀診斷報表，協助教師精準掌握學生個別差異，並提供家長具體之共讀建議，強化家庭閱讀支持系統。