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姓名	Yonatan Garkebo Doyoro(Yonatan Garkebo Doyoro)  查詢紙本館藏	畢業系所	地球科學學系
論文名稱	地電阻剖面影像法之不確定性評估 (Uncertainty Assessment of Geophysical Electrical Resistivity Imaging Methods)
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摘要(中)	地電阻影像剖面法（Electrical Resistivity Imaging Method, ERI）是一種有效重現地下電性構造的技術，但要取得準確的電阻率模型仍具有挑戰性。本研究估算電阻率不確定性並評估常用陣列的對於模型的重現能力：雙偶極（Dipole-Dipole, DD）、單偶極（Pole-Dipole, PD）、溫奈施朗卜吉(Wenner-Schlumberger, WS)及單極法（Pole-Pole, PP）。本研究運用Python模組程式評估各陣列的性能，並進行聯合反演算，降低單一方法的固有不確定性。本研究運用殘差、重複誤差及潛在雜訊、人造物、反演算誤差和模型精度影響評估地電阻量測及模型不確定性。此外，也使用棋盤格模型測試評估空間分辨率和對電阻率擾動的靈敏度，並根據調查深度 (Depth of Investigation, DOI)、異常分析、統計方差和圖像相關性來檢查陣列成像效率。本研究對三相狀態的飽和沉積物（如固體岩石基質、液態水和空氣）進行了 ERI 和 SRT 提供的岩石物理進行聯合反演。ERI 測量不確定性結果顯示，旱季有約 3.2%、濕季有 0.83% 的資料點會高於重複性誤差的3%臨界值，一般來說，在乾季狀態下的地電阻量測會有較高的接地電阻、重複性誤差及殘餘物差，進而造成較多被排除使用的資料點。DOI指數表示模型重現的真實程度，DOI 的閾值深度會隨測量雜訊增加和測量深度改變而減少。基於實驗中的空洞目標物及模型模擬結果顯示，對於較淺的空洞目標物（2.2公尺深），DD陣列的結果有最高的異常效應（1.46）及電阻率變化（24400 .m），PP陣列則有最低的異常效應(0.6)及電阻率變化（2401.m）。反演模型顯示在更深的模型，其解析度和精準度會下降，從而在電阻率模型解釋上產生分歧。本研究推斷 DD 陣列最適合地下目標研究，PD 及WS陣列同樣足夠作為探索目標結構物，然而PP陣列則最為不適合。研究結果顯示，在所有測試的反演模組中（pyGIMLi、BERT、ResIPy 和 SimPEG），較大的異常目標都得能正確解析，然而，在深度3公尺所設置的目標半徑小於 0.5 公尺時，上述模組並沒有顯示任何目標特徵 。以 pyGIMLi、BERT 和 SimPEG 而言，可以重現大於其深度四分之一的目標直徑，而 ResIPy 可以重現大於其深度三分之一倍的目標直徑。 另外，ERT 和 SRT 的聯合反演則充分重現結構，減少了模型的歧義。總體而言，本研究探討了 ERT 測量和模型不確定性，並概述了能有效成像的陣列和反演模組，顯示使用聯合反演算和約束反演算對單一方法的約束，建議未來的將此方法作為應用。
摘要(英)	Although electrical resistivity imaging (ERI) methods are effective for recovering subsurface structures, obtaining an accurate resistivity model is challenging. This study evaluates the recoverability and resistivity uncertainty of commonly used imaging arrays such as dipole-dipole (DD), pole-dipole (PD), Wenner-Schlumberger (WS), and pole-pole (PP). The study also evaluates the performance of free Python-based inversion software and employs joint inversion to reduce the inherent uncertainty of the single method. The resistivity measurement and model uncertainties are investigated using reciprocal error, repeatability error, potential noise, artificial effect, inversion data misfit, and model accuracy. Checkerboard tests are also used to evaluate spatial resolution and sensitivity to resistivity perturbations. The depth of investigation (DOI), anomaly analysis, statistical variances, and image correlations are used to evaluate array imaging efficiencies. The study combines ERI and seismic refraction tomography (SRT) inversion for saturated sediments with three-phase volumetric fractions, such as solid rock matrix, liquid water, and air. According to the ERI measurement uncertainty result, approximately 3.2%t of the dry season datasets and 0.83% of the wet season datasets are above the 3% repeatability error cut-off values. In dry conditions, resistivity measurements generally show high contact resistance, repeatability, and reciprocal errors, resulting in significant data discarding. The DOI threshold depth decreases as measurement noise and survey depths increase. The DD array recovered the highest anomaly index (1.46) and variance (24400 .m) in resistivity data, whereas the PP array recovered the lowest anomaly index (0.60) and variance (2401 .m) for the shallowest target cavity set at 2.2 m depth. At deeper depths, the inverted models exhibit a reduction in model resolution and accuracy, resulting in ambiguity in resistivity model interpretations. Our study shows that the DD array is best suited for subsurface target research. The PD and WS arrays are adequate for surveying target structures, while the PP array is the least suitable. The study results show that while all of the tested inversion freeware packages (pyGIMLi, BERT, ResIPy, and SimPEG) correctly resolve larger anomaly targets, they do not display any signatures for target radius less than 0.5 m set at 3 m depth. ResIPy can reproduce a target diameter greater than one-fourth of its depth, while pyGIMLi, BERT, and SimPEG can recover a target diameter greater than one-fourth of its depth. Furthermore, the joint inversion of ERI and SRT recovered the layer structures adequately, reducing model ambiguity. Overall, this study addresses ERI measurement and model uncertainties, describes effective imaging arrays and inversion freeware packages, and demonstrates single method constraints using joint inversion and constrained inversion, recommending them for future applications.
關鍵字(中)	★ 地電阻影像剖面法 ★ 量測陣列 ★ 數值模型 ★ 不確定性 ★ 開放式反演算軟體 ★ 聯合反演算	關鍵字(英)	★ Electrical Resistivity Imaging ★ Measurement ★ Imaging Arrays ★ Keywords: Electrical Resistivity Imaging, Measurement, Imaging Arrays, Numerical Modelling ★ Uncertainty ★ Inversion Freeware
論文目次	摘要 ii ABSTRACT iii ACKNOWLEDGEMENTS iv TABLE OF CONTENT v LIST OF FIGURES viii CHAPTER ONE 1 INTRODUCTION AND SCOPE OF THE STUDY 1 1.1 Introduction 1 1.2 Aim of this thesis 5 1.3 Structure of the thesis 6 CHAPTER TWO 7 BACKGROUND STUDIES OF THE ELECTRICAL RESISTIVITY METHOD 7 2.1 Introduction 7 2.2 Basic principle 7 2.2.1 One-dimensional resistivity survey 11 2.2.2 Two-dimensional resistivity survey 12 2.2.3 Three-dimensional resistivity survey 13 2.2.4 Common imaging methods 14 2.2.5 The electrical property of subsurface material 17 CHAPTER THREE 21 FORWARD AND INVERSE MODELLING 21 3.1 Background 21 3.2 Forward modelling 21 3.3 Inverse modelling 22 CHAPTER FOUR 28 MEASUREMENT UNCERTAINTY AND ITS PROPAGATION ON THE INVERTED MODEL 28 4.1 Background 28 4.2 Approaches 30 4.3 Results and discussion 32 4.3.1 Measurement noise 32 4.3.1.1 Contact resistance 32 4.3.1.2 Repeatability error 33 4.3.1.3 Reciprocal error 34 4.3.1.4 Potential error 38 4.3.1.5 Artificial noise 39 4.3.1.6 Off-line 3D structure effect on the 2D resistivity measurement 41 4.3.2 Inversion data misfit 44 4.3.3 Model accuracy 46 4.3.4 Estimating noise in the inversion 48 4.4 Summary 51 CHAPTER FIVE 54 UNCERTAINTY OF RESISTIVITY IMAGING IN SURVEYING SUBSURFACE STRUCTURES 54 5.1 Background 54 5.2 Approaches 56 5.3 Resistivity imaging results for subsurface target study 58 5.3.1 Array detection ability 58 5.3.2 Resistivity model results for subsurface cavity 60 5.3.2.1 Depth of cavity 60 5.3.2.2 Size of cavity 67 5.3.2.3 Orientation of cavity 68 5.3.2.4 Geometry of cavity 70 5.3.2.5 Conductive cavity modelling 71 5.3.3 Experimental cavity study 72 5.3.4 Resistivity imaging for dike detection 74 5.3.5 Resistivity imaging for weak zone assessment 76 5.3.6 Spatial resolution 78 5.3.7 Sensitivity to resistivity contrast 82 5.3.8 Summary 86 CHAPTER SIX 88 FREEWARE RESOURCES IN PYTHON FOR ELECTRICAL RESISTIVITY MODELLING 88 6.1 Background 88 6.2 Freeware framework 89 6.2.1 pyGIMLi 90 6.2.2 BERT 91 6.2.3 ResIPy 93 6.2.4 SimPEG 95 6.3 Model experiment 98 6.4 Results and discussion 100 6.4.1 Formulation of apparent resistivity data 101 6.4.2 Evaluation of inverted results 103 6.4.2.1 Layered model 103 6.4.2.2 Target model 104 6.4.3 Freeware summary 109 CHAPTER SEVEN 113 JOINT INVERSION OF ELECTRICAL RESISTIVITY IMAGING AND SEISMIC REFRACTION TOMOGRAPHY 113 7.1 Introduction 113 7.2 Joint inversion 114 7.2.1 Methodology 115 7.2.1.1 Basic principles 115 7.2.1.2 Petrophysical relations 116 7.2.1.3 Model experiment 117 7.2.2 Results and discussion of joint inversion 118 7.2.2.1 Joint inversion for layered model 118 7.2.2.2 Estimating vadose zone 121 7.2.2.3 Regularization constraints 122 7.3 Structurally-constrained inversion 124 7.4 Summary 124 CHAPTER EIGHT 126 STUDY CONCLUSIONS 126 8.1 Concluding remarks 126 8.2 Future research direction 130 8.2.1 Tracing river water recharges using joint inversion 130 8.2.2 Machine learning 130 CHAPTER NINE 132 9.1 Implementation guidelines 132 9.1.1 Designing the survey 132 9.1.2 Data acquisition and verification 133 9.1.3 Data filtering 134 9.1.4 Executing inversion 135 9.1.5 Model interpretation 136 9.2 Application strategy 137 10 BIBLIOGRAPHY 139
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Three-dimensional resistivity tomography in extreme coastal terrain amidst dense cultural signals: application to cliff stability assessment at the historic D-Day site. Geophys. J. Int., 185(1), 201-220. Ullah, S., Zhou, B. and Iqbal, M. A., (2020). A novel electrode array for resistivity imaging to assess groundwater resource: Field test. J. Environ. Eng. Geophys., 15(4), 197-207. Vafidis, A., Economou, N., Ganiatsos, Y., Manakou, M., Poulioudis, G., Sourlas, G., Vrontaki, E., Sarris, A., Guy, M. and Kalpaxis, T., (2005). Integrated geophysical studies at ancient Itanos (Greece). J. Archaeol. Sci., 32(7), 1023-1036. Van Schoor, M., (2002). Detection of sinkholes using 2D electrical resistivity imaging. J. Appl. Geophy., 50(4), 393-399. Verdet, C., Anguy, Y., Sirieix, C., Clément, R. and Gaborieau, C., (2018). On the effect of electrode finiteness in small-scale electrical resistivity imaging. Geophysics, 83(6), EN39-EN52. Vozoff, K. and Jupp, D., (1975). Joint inversion of geophysical data. Geophys. J. Int., 42(3), 977-991. Wagner, F., Mollaret, C., Günther, T., Kemna, A. and Hauck, C., (2019). Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data. Geophys. J. Int., 219(3), 1866-1875. Wagner, F. M. and Uhlemann, S., (2021). An overview of multimethod imaging approaches in environmental geophysics. Adv. Geophys. , 62, 1-72. Ward, W. O., Wilkinson, P. B., Chambers, J. E., Oxby, L. S. and Bai, L., (2014). Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection. Geophys., 197(1), 310-321. Wellmann, F. and Caumon, G., (2018). 3-D Structural geological models: Concepts, methods, and uncertainties, Adv. Geophys. Elsevier, pp. 1-121. Wen, X., Jing, M., Cai, H., Zhang, Y., Hu, S., Teng, Y., Liu, G., Lan, L. and Lu, H., (2020). Temperature characteristics and influence of water-saturated soil resistivity on the HVDC grounding electrode temperature rise. Int. J. Electr. Power Energy Syst., 118, 105720. Werban, U., Attia al Hagrey, S. and Rabbel, W., (2008). Monitoring of root‐zone water content in the laboratory by 2D geoelectrical tomography. J. Plant Nutr. Soil Sci., 171(6), 927-935. Whiteley, J., Chambers, J., Uhlemann, S., Boyd, J., Cimpoiasu, M., Holmes, J., Inauen, C., Watlet, A., Hawley-Sibbett, L. and Sujitapan, C., (2020). Landslide monitoring using seismic refraction tomography–The importance of incorporating topographic variations. J. Eng. Geol., 268, 105525. Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., Haddock, S. H., Huff, K. D., Mitchell, I. M. and Plumbley, M. D., (2014). Best practices for scientific computing. PLoS Comput. Biol., 12(1), e1001745. Wyllie, M. R. J., Gregory, A. R. and Gardner, L. W., (1956). Elastic wave velocities in heterogeneous and porous media. Geophys., 21(1), 41-70. Yang, X., Chen, X., Carrigan, C. R. and Ramirez, A. L., (2014). Uncertainty quantification of CO2 saturation estimated from electrical resistance tomography data at the Cranfield site. Int. J. Greenh. Gas Control., 27, 59-68. Zhang, G., Zhang, G.-B., Chen, C.-c., Chang, P.-Y., Wang, T.-P., Yen, H.-Y., Dong, J.-J., Ni, C.-F., Chen, S.-C. and Chen, C.-W., (2016). Imaging rainfall infiltration processes with the time-lapse electrical resistivity imaging method. Pure Appl. Geophys., 173(6), 2227-2239. Zhang, Y., Wang, B., Lin, G., Ouyang, Y., Wang, T., Xu, S., Song, L. and Wang, R., (2020). Three-Dimensional P-wave Velocity Structure of the Zhuxi Ore Deposit, South China Revealed by Control-Source First-Arrival Tomography. Minerals, 10(2), 148. Zhao, D., Hasegawa, A. and Kanamori, H., (1994). Deep structure of Japan subduction zone as derived from local, regional, and teleseismic events. J. Geophys. Res. Solid Earth, 99(B11), 22313-22329. Zhao, D., Yanada, T., Hasegawa, A., Umino, N. and Wei, W., (2012). Imaging the subducting slabs and mantle upwelling under the Japan Islands. Geophys. J. Int., 190(2), 816-828. Zhou, B. and Dahlin, T., (2003). Properties and effects of measurement errors on 2D resistivity imaging surveying. Near Surf. Geophys., 1(3), 105-117. Zhou, B. and Kanl, I., (2018). Electrical resistivity tomography: a subsurface-imaging technique, Applied geophysics with case studies on environmental, exploration and engineering geophysics. IntechOpen London, UK. Zhou, J., Revil, A., Karaoulis, M., Hale, D., Doetsch, J. and Cuttler, S., (2014). Image-guided inversion of electrical resistivity data. Geophys., 197(1), 292-309. Zhou, W., Beck, B. F. and Adams, A. L., (2002). Effective electrode array in mapping karst hazards in electrical resistivity tomography. J. Environ. Geol., 42(8), 922-928.
指導教授	張竝瑜(Ping-Yu Chang)	審核日期	2023-2-1
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