地電阻影像剖面法常被應用於工程地質與水文地質等領域的議題研究中，是最廣受採用的工程地球物理技術之一。本團隊過去五年間建立的人工與天然電場監測系統，擁有在短時間內可收集大量電位資料，並經由網路即時回傳至研究室雲端硬碟的功能，將傳統地電阻調查工具擴大、延伸成高效能之監測成像技術，以便達成對地層電阻率構造的即時時變掃描工作。透過即時接收大量大地電位資料，研究者不必再頻繁奔走於野外場址與研究室兩地，乃可專注在電阻率影像的解算工作上。然而，伴隨持續回傳的大批電位資料，也同時彰顯了傳統反演解算流程的限制。一來大量的資料造成傳統反算在計算記憶體資源上的負擔，二來密集的電場掃描結果也要求反算速度的提升。本計畫乃提議導入深度學習網路，用以輔助傳統之反演解算流程，希冀此整合流程可以提供更快速、準確的電阻率影像剖面，以強化地電阻影像剖面法於水文地質與工程地質領域的應用。 ;Geo-electrical resistivity imaging method is often used in studies such as engineering geology and hydrogeology, and is one of the most widely used exploration geophysical techniques. The artificial and natural electric field monitoring system established by our team in the past five years has the ability of collecting a large amount of electrical potential data in a short period of time and transferring data to a specified cloud drive via a real-time network. By receiving a large amount of geoelectrical potential data in real time, researchers can focus on the calculation of inverting electrical resistivity structures subsurface. However, the large amount of electrical data continuously and intensively transferring back to the laboratory also highlights the limitations of the traditional inversion procedures. Firstly, a large amount of data causes a burden on the memory resources in the traditional inverse calculation. Secondly, the intensive scanning results also highly require an increase in the inverse calculation speed. This project therefore proposes the introduction of a deep learning network to assist the traditional inversion process. It is expected that this integration process can provide faster and more accurate resistivity images to enhance the electrical resistivity imaging method in applications of hydrogeology and engineering geology.
