運用馬可夫鍊隨機場模擬與合成地質模型評估地質模型不確定性

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謝澤銘(CHE CHAK MENG) 查詢紙本館藏

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應用地質研究所

論文名稱

運用馬可夫鍊隨機場模擬與合成地質模型評估地質模型不確定性

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摘要(中)

建構符合現地狀態之地質模型(Geological Model)將有助於土木、大地、地下構造物、壩體等工程設計依據，並且可提供地質相關災害之分析與風險評估。藉由各式現地取樣資料建構之地質模型，由於經費限制或其他因素導致資料量的多寡直接影響地質模型之擬真程度。或因工程師地質專業及使用套裝軟體內插之侷限性，各式地質模型將具備其各種程度之不確定性(Uncertainty)。
本研究先建立合成地質模型作為資料基礎，然後取樣模型資料進行馬可夫鍊隨機場(Markov chain random field)計算，進而探討評估地質模型不確定性之方法。以場址為嘉義縣民雄鄉民雄、頭橋工業區附近區域為模型研究案例，使用地質鑽探岩心資料為基礎，由實際現地資料建構合成(Synthetic)地質模型，搭配前人研究在此區域的地質研究和結合地質知識，建立出符合地質學邏輯的合成地質模型，然後取樣模型資料，進行馬可夫鍊隨機場的敏感性分析，從中考慮如何設計馬可夫鍊隨機場的模型參數，進而量化地質模型之亂度(Entropy)以表示地質模型不確定性，利用最大概似估計(Maximum Likelihood Estimation)和地層邊界殘差分析呈現模擬模型的準確性，以評估模擬模型之最佳參數，找出地層傾斜角度和材料側向延伸性。
最後從結果討論，亂度量化的不確定性反映的是資料計算的先天變異性，而從最大概似評估在地質條件參數組合都能達到最佳值的結果來看，最大概似評估為最能評估地質學原理對地質模型不確定性之貢獻的方法。

摘要(英)

Simplifying the geological model that conforms to the current state of the ground will help the engineering design basis of civil engineering, geotechnical engineering, underground structures, and dams, and provide analysis and risk assessment of geological disasters. However, there are some difficulties with constructing geological models. For example, there are few data collected from samples or the limitation of engineers and software. These reasons all affect the uncertainty of model.
In this study, a synthetic geological model is established as the data basis, and then the model data is sampled for Markov chain random field calculation, and then methods for evaluating the uncertainty of the geological model are discussed. Taking the site is Minxiong Township, Chiayi County, and the vicinity of Touqiao Industrial Park as a model study case. Based on geological drilling core data, a synthetic geological model is constructed from actual on-site data, combined with previous research here. Regional geological research and geological knowledge are combined to establish a synthetic geological model that conforms to geological logic. After sampling the model data to conduct sensitivity analysis of the Markov chain random field, consider how to design the model parameters of the Markov chain random field. Quantify the entropy of the geological model, evaluate the Maximum Likelihood Estimation of the geological model to show the uncertainty of the geological model, use the maximum likelihood estimation and the residual analysis of the stratigraphy boundary shows the accuracy of the simulation model to evaluate the effect of the best parameters. Find out the inclination angle of the formation and the lateral continuity of the material. Finally, discuss how geological principles can reduce the uncertainty of the geological model.

關鍵字(中)

★ 合成地質模型
★ 地質模型不確定性
★ 不確定性量化
★ 地質知識
★ 亂度
★ 最大概似估計

關鍵字(英)

★ Synthetic geological model
★ Geological model uncertainty
★ Uncertainty quantification
★ Geological knowledge
★ Entropy
★ Maximum Likelihood Estimation

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 viii
表目錄 xiii
符號表 xiv
一、緒論 1
1.1 研究動機與目的 1
1.2 研究架構 4
二、文獻回顧 6
2.1 地質模型的不確定性 6
2.2 地質模型不確定性之量化 10
2.2.1 定性描述地質模型不確定性 10
2.2.2 利用評分系統量化地質模型變異係數 11
2.2.3 給定模型參數以隨機場生成大量地質模型評估不確定性 12
2.3 建立地質模型的套裝軟體 15
2.4 沉積地質的側向延伸 16
三、研究方法及案例分析 17
3.1 合成地質模型研究區域概況 19
3.1.1. 沉積環境的演變 19
3.1.2. 鑽井資料判讀 24
3.2 建立三維合成地質模型 25
3.2.1 套裝軟體建模過程 25
3.2.2 傳統網格空間內插計算方法建模 26
3.3 隨機場生成地質模型方法 30
3.3.1 馬可夫鍊隨機場模擬方法 30
3.3.2 亂度量化不確定性方法 33
3.3.3 馬可夫鍊隨機場敏感度分析 34
3.4 馬可夫鍊隨機場的準確性計算方法 35
3.4.1 最大概似估計 35
3.4.2 地層邊界殘差分析及其標準差統計 37
3.5 研究案例 39
3.5.1 沖積層沉積地質環境 39
3.5.2 紅土台地堆積層地質環境 40
四、結果與討論 42
4.1 三維合成地質模型 42
4.2 敏感度分析之結果 49
4.2.1 馬可夫鍊模擬實現數量的決定 49
4.2.2 網格形狀(水平長/垂直長)與空間關聯異向性參數a的相互關係 52
4.3 鑽井密度與空間關聯異向性參數a對不確定性之影響 56
4.4 估計空間關聯異向性參數a以及地層傾斜角度α之方法 58
4.5 合成地質模型案例評估不確定性 64
4.5.1 近水平沉積地質案例 64
4.5.2 紅土台地地質案例 72
五、結論與建議 77
5.1 結論 77
5.2 建議 78
參考文獻 79
附錄A、地質鑽孔井錄 86
附錄B、馬可夫鍊隨機場迭代次數的確定 95

參考文獻

[1] 中央地質調查所工程地質探勘資料庫，2021。檢自https://geotech.moeacgs.gov.tw/imoeagis/Home/Map#
[2] 中央地質調查所水文地質資料庫，2021。檢自https://hydrogis.moeacgs.gov.tw/map/zh-tw#
[3] 王豐仁，2020，「地質模型能有多準確?-地質模型不確定衍生之風險在工程、環境、水資源和地質災害之探討」，國立中央大學應用地質研究所，博士候選人研究計畫書。
[4] 吳琮壬，2007，「嘉義海岸平原區末次冰期以來古沉積環境之研究」，國立臺灣大學理學院地質科學研究所，碩士論文。
[5] 徐雅涵、盧育辰、莊長賢、黃俊鴻，2020，「地質模型不確定性對離岸風機基礎垂直承載力之影響」，第18屆大地工程學術研究討論會論文集。
[6] 陳文山、葉明官、楊志成、石瑞銓、林啟文、侯進雄，2006，「梅山斷層的構造特性」，經濟部中央地質調查所彙刊，第十九號，第135-151頁。
[7] 馮豐隆、黃志成，1997，「空間模式應用於林分結構母數推估之研究」，國立中興大學，實驗林研究彙刊，19:2卷，第57-75頁。
[8] 蔡玉琴，1997，「淡水河流域降雨時空分析及推估:地理資訊系統的應用」，師大地理研究報告26期，第139-158、171-177、192-201頁。
[9] 蔡孟儒，2020，「水文地質概念模型差異對污染傳輸模擬之影響」，國立中央大學應用地質研究所，碩士論文。
[10] Baojun, W., Bin, S., Zhen, S., 2009. A simple approach to 3D geological modelling and visualization. Bulletin of Engineering Geology and the Environment, 68(4), 559-565.
[11] Bardossy, G., Fodor, J., 2001. Traditional and new ways to handle uncertainty in geology. Natural Resources Research, 10(3), 179-187.
[12] Boyd, D. L., Walton, G., Trainor-Guitton, W., 2019. Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist′s cross-section. Engineering Geology, 260, 105246.
[13] Burland, J. B., 1987. The teaching of soil mechanics - a personal view. The Nash Lecture, Proceedings of the 9th European Conference on Soil Mechanics and Foundation Engineering, Dublin, Ireland, 3,1427-1441.
[14] Calcagno, P., Chilès, J. P., Courrioux, G., Guillen, A., 2008. Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules. Physics of the Earth and Planetary Interiors, 171, 147-157.
[15] Cowan, E. J., Beatson, R. K., Fright, W. R., McLennan, T. J., Mitchell, T. J., 2002. Rapid geological modelling. Applied Structural Geology for Mineral Exploration and Mining, 36, 39-41.
[16] De la Varga, M., Schaaf, A., Wellmann, F., 2019. GemPy 1.0: open-source stochastic geological modeling and inversion. Geoscientific Model Development, 12(1), 1-32.
[17] Dott, R. H., Batten, R. L., 1976. Evolution of the earth, McGraw-Hill, New York, ISBN 0070176191, 2nd edition, 504.
[18] FGDC, 2006. FGDC digital cartographic standard for geologic map symbolization. US Geological Survey Geologic Data Subcommittee, Federal Geographic Data Committee Document Number FGDC-STD-013-2006, 33.
[19] FHWA, 2011. Subsurface utility engineering. Federal Highway Administration, US Department of Transportation. http://www.fhwa.dot.gov/programadmin/sueindex.cfm
[20] Fookes, P. G., 1997. Geology for Engineers: the Geological Model, Prediction and Performance. Quarterly Journal of Engineering Geology and Hydrogeology, 30, 293-424.
[21] Geman, S., Geman, D., 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Pattern Anal. Mach. Intell. IEEE Trans. 721-741.
[22] Gong, W., Tang, H., Wang, H., Wang, X., Juang, C. H., 2019. Probabilistic analysis and design of stabilizing piles in slope considering stratigraphic uncertainty. Engineering Geology, 259, 105162.
[23] Hsu, Y. H., Lu, Y. C., Khoshnevisan, S., Juang, C. H., Hwang, J. H., 2021. Effect of geological uncertainties on the design of offshore wind turbine foundations. Engineering Geology. (under review)
[24] Jalees, M. I., Farooq, M. U., Anis, M., Hussain, G., Iqbal, A., Saleem, S., 2020. Hydrochemistry modelling: evaluation of groundwater quality deterioration due to anthropogenic activities in Lahore, Pakistan. Environment, Development and Sustainability, 23(3), 3062-3076.
[25] Juang, C. H., Khoshnevisan, S., Zhang, J., 2015. Maximum likelihood principle and its application in soil liquefaction assessment. Chapter 4, in Risk and Reliability in Geotechnical Engineering, 181-220.
[26] Keaton, J. R., 2013. Engineering geology: Fundamental input or random variable? In: Withiam JL, Phoon KK, Hussein MH (eds) Foundation engineering in the face of uncertainty. ASCE, Reston, 232-253.
[27] Keaton, J. R., 2015. A Suggested Geologic Model Complexity Rating System. Engineering Geology for Society and Territory - Volume 6: Applied Geology for Major Engineering Projects. 363-366.
[28] Knill, J., 2003. Core Values: The First Hans-Cloos Lecture. Bull. Eng. Geol. Environ. 62(1), 1-34.
[29] Lekula, M., Lubczynski, M. W., Shemang, E. M., 2018. Hydrogeological conceptual model of large and complex sedimentary aquifer systems-Central Kalahari Basin (Botswana). Physics and Chemistry of the Earth, Parts A/B/C, 106, 47-62.
[30] Lemon, A. M., Jones, N. L., 2003. Building solid models from boreholes and user-defined cross-sections. Computers & Geosciences, 29(5), 547-555.
[31] Li, Z., Wang, X. R., Wang, H., Liang, R. Y., 2016. Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field. Engineering Geology, 201, 106-122.
[32] Lu, Y. C., 2020. Personal Communication, a meeting of Markov random field.
[33] Mann, C. J., 1993. Uncertainty in geology. In: Davis, J. C. and Herzfeld, U. C. (editors): Computers in Geology - 25 Years of Progress, 241-254.
[34] Matheron G., 1971. The Theory of Regionalized Variables and Its Applications. Les Cahiers du Centre de Morphologie Mathematique, No.5, Ecole des Mines de Paris, Paris.
[35] Morgenstern, N. R., Cruden, DM, 1977. Description and classification of geotechnical complexities. In: International symposium on the geotechnics of structurally complex formations, Vol. 2, Italian Geotechnical Society, 195-204.
[36] Morgenstern, N. R., 2000. Common ground. In: GeoEng2000: An International Conference on Geotechnical and Geological Engeering. Technomic Publishing Co., Lancaster, Pennsylvania, USA, 1-20.
[37] NRCS, 2002. Rock material field classification system. US Department of Agriculture Natural Resources Conservation Service, National Engineering Handbook Part 631 Geology, Chapter 12. http://directives.sc.egov.usda.gov/viewerFS.aspx?hid=21423
[38] Parry, S., Baynes, F. J., Culshaw, M. G., Eggers, M., Keaton, J. F., Lentfer, K., Novotny, J., Paul, D., 2014. Engineering geological models: an introduction: IAEG commission 25. Bulletin of Engineering Geology and the Environment, 73, 689-706.
[39] Pierson, LA, van Vickle, R, 1993. Rockfall hazard rating system-participants’ manual. Federal Highway Administration Publication No. FHWA-SA-93-057, Washington, DC.
[40] Qi, X. H., Li, D. Q., Phoon, K.K., Cao, Z. J., Tang, X. S., 2016. Simulation of geologic uncertainty using coupled Markov chain. Engineering Geology, 207, 129-140.
[41] Saad, R., Nawawi, M. N. M., Mohamad, E.T., 2012. Groundwater detection in alluvium using 2-D electrical resistivity tomography (ERT). Electronic Journal of Geotechnical Engineering, 17, 369-376.
[42] Sandersen, P. B. E., 2008. Uncertainty assessment of geological models-A qualitative approach. IAHS Publications Series of Proceedings and Reports, 320, 345-349.
[43] Shishaye, H. A., Tait, D. R., Befus, K. M., Maher, D. T., Reading, M. J., 2020. New insights into the hydrogeology and groundwater flow in the Great Barrier Reef catchment, Australia, revealed through 3D modelling. Journal of Hydrology: Regional Studies 30, 1-17.
[44] Sullivan, T. D., 2010. The geological model. Geologically Active - Proceedings of the 11th Congress of the International Association for Engineering Geology and the Environment, 155-170.
[45] Trabelsi, F., Tarhouni, J., Mammou, A. B., Ranieri, G., 2011. GIS-based subsurface databases and 3-D geological modeling as a tool for the set up of hydrogeological framework: Nabeul-Hammamet coastal aquifer case study (Northeast Tunisia). Environmental Earth Sciences, 70(5), 2087-2105.
[46] USING THE COMBINED SESOIL/AT123D, 2014. New Jersey Department of Environmental Protection.
[47] Wang, X., Li, Z., Wang, H., Rong, Q., Liang, R. Y., 2016. Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Structural Safety, 62, 88-100.
[48] Wang, X., Wang, H., Liang, R. Y., 2017. A method for slope stability analysis considering subsurface stratigraphic uncertainty. Landslides, 15(5), 925-936.
[49] Wang, X., 2020. Uncertainty quantification and reduction in the characterization of subsurface stratigraphy using limited geotechnical investigation data. Underground Space, 5(2), 125-143.
[50] Wellmann, J. F., Horowitz, F. G., Schill, E., Regenauer-Lieb, K., 2010. Towards incorporating uncertainty of structural data in 3D geological inversion. Tectonophysics, 490(3-4), 141-151.
[51] Wellmann, J. F., Lindsay, M., Poh, J., Jessell, M., 2014. Validating 3-D structural models with geological knowledge for improved uncertainty evaluations. Energy Procedia, 59, 374-381.
[52] Wilding, L. P., Drees, L. R., 1983. Spatial Variability and Pedology. Pedogenesis and Soil Taxonomy - I. Concepts and Interactions, 83-116.
[53] Willis D. W., John L. S., 2001. Manual of applied field hydrogeology. McGraw-Hill education, New York.

指導教授

董家鈞(Jia-Jyun Dong)

審核日期

2021-10-13

推文