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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/89390

    Title: 從電阻數據預測含水量;Prediction of Water Content from Electrical Resistance Data
    Authors: 陳萬友;Huu, Tran Van
    Contributors: 地球科學學系
    Keywords: 電阻率斷層掃描;電阻數據;含水量;預言;典型相關分析;主成分;Electrical resistivity tomography;electrical resistance data;water content;prediction;canonical correlation analysis;principal component
    Date: 2022-09-28
    Issue Date: 2022-10-04 11:12:59 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來,電阻率層析成像(ERT)方法在地下水勘探、水文地質調查和滑坡監測等許多領域取得了成功。 ERT 的電阻數據受鹽度、溫度和水飽和度的影響。因此,電阻數據包含地下的隱藏信息。在本論文中,將通過電阻數據直接使用改進的以預測為中心的方法來預測地下的含水量。目標是根據電阻數據預測降雨事件中的水含量。首先,將通過岩石物理關係與來自乾燥時刻的電阻數據的反演電阻率數據創建先前的含水量數據集。然後,可以通過主成分分析對電阻數據和含水量數據進行降維,以保留兩個數據集中信息量最大的部分。接下來,通過典型相關分析(CCA)建立阻力數據和含水量數據之間的線性關係,可以從CCA空間的阻力數據得分推導出降雨時間的含水量。該工作流程已應用於台灣北部鯉魚潭地區的延時 ERT。結果表明,已經成功地從電阻數據預測了降雨事件中的含水量,並且含水量的變化對降雨降水很敏感。預測的含水量是研究區構造因素的指示。;In recent years, Electrical Resistivity Tomography (ERT) method has gained success in many sectors, such as groundwater exploration, hydrogeology surveys, and monitoring landslides. Electrical resistance data from ERT are affected by salinity, temperature, and water saturation. Hence, Electrical resistance data contains the hidden information of the subsurface. In this thesis, the water content in the subsurface will be predicted by electrical resistance data directly using a modified prediction-focused approach. The goal is to forecast water content in the rainfall event from electrical resistance data. First, prior set of water content data will be created by petrophysical relationships with inverted resistivity data from electrical resistance data in the dry moment. Then, both electrical resistance data and water content data could be reduced their dimension through principal component analysis to keep the most informative part of the two datasets. Next, a linear relationship between resistance data and water content data would be established by canonical correlation analysis (CCA), the water content in rain time can be derived from resistance data scores in CCA space. The workflow has been applied to the time-lapse ERT in the Liyutan area, Northern Taiwan. Results show that water content in rain events has been successfully predicted from electrical resistance data and water content changes are sensitive to rainfall precipitation. The predicted water content is an indication of tectonic factors in the research area.
    Appears in Collections:[地球物理研究所] 博碩士論文

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