全球氣候變遷引發的海平面上升已成為沿海地區面臨的嚴峻挑戰。潮位站作為監測海水面變化的關鍵設施，其長期觀測紀錄卻常受到儀器更換、測站遷移及碼頭改建等因素影響，產生非海洋真實變化的基準偏移(Datum Shift)。若未能準確偵測並校正這些基準偏移，將嚴重影響長期海平面上升速率之估算與相關科學研究的準確性。本研究旨在發展一套針對潮位資料之基準偏移自動偵測與改正整合演算法。本系統整合滑動視窗均值偵測法、多解析度偵測法及平滑梯度偵測法，透過多元方法的交叉驗證與多數決投票機制，有效降低單一方法在複雜海況與極端訊號下的誤判率，精確鎖定偏移跳變點。此外，本研究建立基準偏移改正機制，透過調和分析結合步階函數，有效分離真實海平面變動與測站基準跳動。本研究選取基隆、高雄、臺中港及馬祖四個具有不同潮汐特性與環境背景之潮位站進行模擬實驗，驗證演算法在不同基準偏移情境下之性能。研究結果顯示：在資料前處理方面，相較於單站移除潮汐資料以及移除潮汐資料和動態大氣擾動之前處理，採用鄰近潮位站差值之前處理能有效消除大範圍共同環境訊號，顯著提升基準偏移偵測之成功率；在多演算法整合效能方面，透過多元演算法投票機制，能有效過濾隨機誤差，在基隆、高雄及臺中港等測站，基準偏移偵測成功率最高可達90%以上；此外，針對長期海平面趨勢與基準偏移改正，研究證實所選的四個測站與模擬情境而言，偏移參數估計在資料長度增加至約15年後，呈現相對穩定的現象，且本島三站(基隆、高雄與臺中港)之基準偏移回復率可達86.1%至99.7%，確保了歷史潮位資料校正之統計顯著性與物理正確性。
本研究所發展之自動偵測與改正系統，可有效還原真實且可靠的海平面水位變化，為臺灣沿海防災策略擬定與氣候變遷長期研究提供關鍵的數據支持。

Sea-level rise caused by global climate change poses severe threats to coastal regions. Tide gauges are essential tools for monitoring sea-level variations; however, their long-term records are frequently compromised by datum shifts resulting from instrument replacements, station relocations, structural modifications, and vertical land motion. Failing to accurately detect and correct these discontinuities can lead to significant biases in estimating long-term sea-level rise, thereby undermining scientific research and informed policymaking.
This study develops an integrated algorithm for the automatic detection and correction of datum shifts in high-temporal-resolution tide gauge data. The system combines three distinct detection methods—the Double-Window Mean Shift Detection, Multi-Scale Difference Detection, and Edge Detection—utilizing cross-validation and a voting mechanism to enhance robustness against complex marine environments and extreme noise. Furthermore, a correction framework combining harmonic analysis and step functions is implemented to isolate true sea-level variations from instrumental datum shifts.

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