從電阻數據預測含水量

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姓名

陳萬友(Tran Van Huu) 查詢紙本館藏

畢業系所

地球科學學系

論文名稱

從電阻數據預測含水量
(Prediction of Water Content from Electrical Resistance Data)

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至系統瀏覽論文 (2025-10-1以後開放)

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 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

關鍵字(英)

★ Electrical resistivity tomography
★ electrical resistance data
★ water content
★ prediction
★ canonical correlation analysis
★ principal component

論文目次

中文摘要 i
Abstract ii
Acknowledgements iii
Table of Content iv
List of Figures vi
Explanation of Symbols xii
Chapter 1 Introduction 1
1-1 Purpose and motivation 1
1-2 Study area 1
1-3 Structure of the thesis 3
Chapter 2 Data and Method 11
2-1 Rainfall precipitation data 11
2-2 Groundwater table data 12
2-3 Electrical resistivity tomography 14
2-3-1 Electrical Resistivity Tomography method 14
2-3-2 ERT array 15
2-3-3 ERT inversion 22
2-4 Prediction-Focused approach 32
2-4-1 Generating a prior set of relative water content 33
2-4-2 Dimension reduction of Resistance data and water content model 39
2-4-3 Linearizing relationship between Resistance data and water content model 47
2-4-4 Predicting Water content changes from Resistance data 55
2-4-5 Validation 76
Chapter 3 Results and Discussions 77
3-1 Estimation of Water content 77
3-2 Estimation of water saturation 92
3-3 Estimated water content response to groundwater table level 94
3-4 Relations between water content and geological information 105
Chapter 4 Conclusions 107
References 109
Appendix A – Canonical correlation analysis 112
Appendix B – Validations results 115
Appendix C – Predicted water saturation 182

參考文獻

AGI, 2009. Instruction Manual for EarthImager 2D version 2.4.0. Advanced Geosciences, Inc., Austin, Texas,.
Archie, G.E., 1942. The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics. Trans. AIME 146, 54–62. https://doi.org/10.2118/942054-G
Claerbout, J.F., Muir, F., 1973. Robust modeling with erractic data. GEOPHYSICS 38, 826–844. https://doi.org/10.1190/1.1440378
Dahlin, T., Zhou, B., 2004. A numerical comparison of 2D resistivity imaging with 10 electrode arrays. Geophys. Prospect. 52, 379–398. https://doi.org/10.1111/j.1365-2478.2004.00423.x
Dey, A., Morrison, H.F., 1979. Resistivity modeling for arbitrarily shaped two-dimensinal structures. Geophys. Prospect. 27, 106–136. https://doi.org/10.1111/j.1365-2478.1979.tb00961.x
Ellis, R.G., Oldenburg, D.W., 1994. Applied geophysical inversion. Geophys. J. Int. 116, 5–11. https://doi.org/10.1111/j.1365-246X.1994.tb02122.x
Fetter, C.W., 2018. Applied hydrogeology.
Hermans, T., Oware, E., Caers, J., 2016. Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data. Water Resour. Res. 52, 7262–7283. https://doi.org/10.1002/2016WR019126
Hotelling, H., 1936. Relations Between Two Sets of Variates. Biometrika 28, 321–377. https://doi.org/10.2307/2333955
Hsu, Y.-J., Lai, Y.-R., You, R.-J., Chen, H.-Y., Teng, L.S., Tsai, Y.-C., Tang, C.-H., Su, H.-H., 2018. Detecting rock uplift across southern Taiwan mountain belt by integrated GPS and leveling data. Tectonophysics 744, 275–284. https://doi.org/10.1016/j.tecto.2018.07.012
Imamura, S., Inazaki, T., Kita, T., Sakanishi, K., 2013. Development and application of improved high-speed resistivity profiling system using code division multiple transmission, in: Proceedings of the 11th SEGJ International Symposium, Yokohama, Japan, 18-21 November 2013, SEG Global Meeting Abstracts. Society of Exploration Geophysicists of Japan, pp. 68–71. https://doi.org/10.1190/segj112013-018
Koefoed, Otto., Patra, H.P., Mallick, K., 1979. Geosounding principles 1: Resistivity Sounding Measurements. Elsevier, Amsterdam.
Liming Engineering Consultants Co., Ltd., 2018. Liyutan Reservoir Monitoring and Safety Inspection in year 107 - Reservoir safety monitoring and analysis. (Investment in right bank slope improvement review and planning and design report (final version)). Republic of China.
Lines, L.R., Treitel, S., 1984. A review of least-squares inversion and its application to Geophysical Problems. Geophys. Prospect. 32, 159–186. https://doi.org/10.1111/j.1365-2478.1984.tb00726.x
Loke, M., 2004. Tutorial : 2-D and 3-D electrical imaging surveys.
Loke, M.H., Chambers, J.E., Rucker, D.F., Kuras, O., Wilkinson, P.B., 2013. Recent developments in the direct-current geoelectrical imaging method. J. Appl. Geophys. 95, 135–156. https://doi.org/10.1016/j.jappgeo.2013.02.017
Nagel, S., Granjeon, D., Willett, S., Lin, A.T.-S., Castelltort, S., 2018. Stratigraphic modeling of the Western Taiwan foreland basin: Sediment flux from a growing mountain range and tectonic implications. Mar. Pet. Geol. 96, 331–347. https://doi.org/10.1016/j.marpetgeo.2018.05.034
Pidlisecky, A., Knight, R., 2008. FW2_5D: A MATLAB 2.5-D electrical resistivity modeling code. Comput. Geosci. 34, 1645–1654. https://doi.org/10.1016/j.cageo.2008.04.001
S. V. Schell, W. A. Gardner, 1995. Programmable canonical correlation analysis: a flexible framework for blind adaptive spatial filtering. IEEE Trans. Signal Process. 43, 2898–2908. https://doi.org/10.1109/78.476433
Satija, A., Caers, J., 2015. Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space. Adv. Water Resour. 77, 69–81. https://doi.org/10.1016/j.advwatres.2015.01.002
Tarantola, A., 2004. Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics.
Uurtio, V., Monteiro, J.M., Kandola, J.S., Shawe-Taylor, J., Fernández-Reyes, D., Rousu, J., 2018. A Tutorial on Canonical Correlation Methods. ACM Comput. Surv. CSUR 50, 1–33.
Wahab, S., Saibi, H., Mizunaga, H., 2021. Groundwater aquifer detection using the electrical resistivity method at Ito Campus, Kyushu University (Fukuoka, Japan). Geosci. Lett. 8, 15. https://doi.org/10.1186/s40562-021-00188-6
Zhang, G., Zhang, G.-B., Chen, C., Chang, P.-Y., Wang, T.-P., Yen, H.-Y., Dong, J.-J., Ni, C.-F., Chen, S.-C., Chen, C.-W., Jia, Z., 2016. Imaging Rainfall Infiltration Processes with the Time-Lapse Electrical Resistivity Imaging Method. Pure Appl. Geophys. 173, 2227–2239. https://doi.org/10.1007/s00024-016-1251-x
Zhou, B., 2019. Electrical Resistivity Tomography: A Subsurface-Imaging Technique, in: Applied Geophysics with Case Studies on Environmental, Exploration and Engineering Geophysics. IntechOpen, Rijeka, p. Ch. 7. https://doi.org/10.5772/intechopen.81511

指導教授

陳建志(Chen Chien-Chih)

審核日期

2022-9-28

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