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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 余化龍 | |
| dc.contributor.author | Meng-Ting Wu | en |
| dc.contributor.author | 吳孟庭 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:08:52Z | - |
| dc.date.available | 2017-08-25 | |
| dc.date.copyright | 2015-08-25 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-19 | |
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Carnegie Mellon University: School of Computer Science. 35. P. Bogaert, M.-A. W. (2004). Combining categorical and continuous information using Bayesian Maximum Entropy. Université catholique de Louvain: dept. of Environmental Sciences and Land Use Planning – Environmetry and Geomatics,. 36. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(7-12), 559-572. 37. Rosenfeld, R. (1994). Adaptive Statistical Language Modeling: A Maximum Entropy Approach. (Ph.D. thesis), Carnegie Mellon University. 38. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana,: University of Illinois Press. 39. Stephen Della Pietra, V. D. P., John Lafferty. (1997). Inducing Features of Random Fields. Paper presented at the IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 40. Venables, W. N., & Ripley, B. D. (1994). Modern applied statistics with S-Plus. New York: Springer-Verlag. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52163 | - |
| dc.description.abstract | 在自然環境科學和工程應用等研究領域中，在分析地質、水文、地下水或是洪旱災時，對研究區域的地質組成岩性有一定的了解，能夠使研究方向和觀念更加正確，進而幫助研究順利進行。但由於受到許多自然和人為的條件侷限，導致實際採樣和觀測資料相當有限，許多研究僅能使用有限的觀測資料進行分析。為有效了解未知空間點的岩性分布，需透過空間統計方法來對未知點進行推估，傳統上有許多地理統計方法應用於此，而本研究應用的貝氏最大熵法(Bayesian Maximum Entropy, BME method)為其中之一新興的時空間地理統計方法。其結合數值模式方法與資料導向方法，可以同時考慮時間和空間得相關性，並以貝氏條件機率的概念為基礎，結合物理知識和其他不確定性資料以增強推估資訊，同時對序率資料與空間資料進行推估分析。而岩性分類的地質資料屬於離散分布的類別型資料，因此本研究應用「類別型貝氏最大熵法(Categorical BME)」，並同時考慮限制式可能存在的不確定性，嘗試放鬆限制式的不確定性範圍後進行模式收斂，以針對岩性類別資料進行推估分析。透過地質鑽探所取得之有限的岩心資料，建立完整的三維類別型貝氏最大熵法岩性推估模式，推估台北盆地之三維岩性分布結果，期望可供台北盆地地質未來相關研究之參考。 | zh_TW |
| dc.description.abstract | In environmental scientific applications and studies, we have to understand the geological lithological composition in the study area. Because of some restrictions of the situation in reality, only limited amount of data can be acquired. In order to find out the lithological distribution in the study area, many geological spatial statistical methods used to analyze the lithological composition on unsampled points or grids. This study applied the Bayesian Maximum Entropy (the BME method), which is a new method in the geological spatiotemporal statistics field. The BME method can compute the spatiotemporal correlation of the data, and integrate not only the hard data but the soft data to improve the results of estimation. The data of lithological classification is discrete categorical data. Therefore, this research attempt to apply Categorical BME to establish a complete three-dimensional Lithological estimation model. And try to regularize the maximum entropy density estimation. Apply the limited hard data from the cores and the soft data generated from the geological dating data and the virtual wells to estimate the three-dimensional lithological classification in Taipei Basin. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:08:52Z (GMT). No. of bitstreams: 1 ntu-104-R02622006-1.pdf: 4485848 bytes, checksum: 9660f73b76309243e1eb4554bd557db6 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 摘要 I
Abstract II 目錄 III 圖目錄 VI 表目錄 VIII 第一章 緒論 1 1.1 研究緣起 1 1.2 研究目的 2 第二章 文獻回顧 3 2.1 地質岩性推估模式 3 2.2 類別型貝氏最大熵法應用 7 第三章 理論概述 13 3.1 類別型離散資料 13 3.1.1 類別型隨機場 Categorical random field 13 3.1.2 共變異函數和變異 14 3.2 類別型貝氏最大熵法 17 3.2.1 熵 Entropy 17 3.2.2 最大熵法 Maximum Entropy (ME) 18 3.2.3 貝氏最大熵法 Bayesian Maximum Entropy (BME) 20 3.2.4 類別型貝式最大熵法 Categorical BME 22 3.3 迭代演算法Iterative scaling 23 3.3.1 通用迭代演算法GIS 26 3.3.2 改進式迭代演算法IIS 27 3.3.3 自助抽樣法Bootstrap Method 29 3.3.4 最大熵推估正規化 Regularization 30 3.4 經驗正交函數法EOF 32 3.5 模式評估指標 36 3.5.1 交叉驗證 Cross Validation 36 3.5.2 傳統地質剖面圖 36 第四章 研究區域之資料蒐集與概況分析 37 4.1 研究區域介紹 37 4.2 資料蒐集與前處理 38 4.2.1 岩心資料 39 4.2.2 定年資料 41 4.2.3 虛擬井資料 43 4.2.4 EOF資料 44 第五章 岩性推估模式建置 49 5.1 研究流程 49 5.2 前置分析 50 5.2.1 岩性空間相關性分析 52 5.2.2 岩性時間相關性分析 54 5.2.3 EOF相關性分析 56 5.3 三維岩性推估整合模式建置 59 5.3.1 確定和不確定性資料輸入之推估參數設定 59 5.3.2 整合推估模式建置 65 第六章 結果與討論 68 6.1 應用於三維岩性推估結果 68 6.2 研究總結與建議 73 第七章 參考文獻 76 | |
| dc.language.iso | zh-TW | |
| dc.subject | 迭代演算法 | zh_TW |
| dc.subject | 類別型貝氏最大熵法 | zh_TW |
| dc.subject | 地質岩性分類 | zh_TW |
| dc.subject | 水文地質架構 | zh_TW |
| dc.subject | 限制式條件正規化 | zh_TW |
| dc.subject | Regularization | en |
| dc.subject | Categorical Bayesian Maximum Entropy method | en |
| dc.subject | Lithological Classification | en |
| dc.subject | Iterative scaling | en |
| dc.subject | Hydrogeological Setting | en |
| dc.title | 應用貝氏最大熵法於臺北盆地水文地質推估 | zh_TW |
| dc.title | Estimation of Lithological Classification in Taipei Basin:
A Bayesian Maximum Entropy Method | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳主惠,張良正,張?瑜 | |
| dc.subject.keyword | 類別型貝氏最大熵法,地質岩性分類,水文地質架構,限制式條件正規化,迭代演算法, | zh_TW |
| dc.subject.keyword | Categorical Bayesian Maximum Entropy method,Lithological Classification,Hydrogeological Setting,Regularization,Iterative scaling, | en |
| dc.relation.page | 79 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-08-19 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物環境系統工程學研究所 | zh_TW |
| Appears in Collections: | 生物環境系統工程學系 | |
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| ntu-104-1.pdf Restricted Access | 4.38 MB | Adobe PDF |
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