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研究生:蔡嘉羚
研究生(外文):Chia-Ling Tasi
論文名稱:基於學習校正傳遞時間的地震定位系統
論文名稱(外文):Earthquake Locating with Learning-Based Traveling Time Calibration
指導教授:黃俊郎黃俊郎引用關係
指導教授(外文):Jiun-Lang Huang
口試委員:吳逸民金台齡
口試日期:2015-07-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電子工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:44
中文關鍵詞:地震定位機器學習偏移量校正
外文關鍵詞:Earthquake locatingMachine learningCalibration
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  • 被引用被引用:0
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好的地震定位系統需要精確且快速的定位出地震的發震位置,有準確的測站觸發時間跟能模擬實際地震波傳遞情形的速度模型能夠提高定位的精準度。本研究著重在如何取得能模擬實際地震波傳遞情形的速度模型來提高定位的準確度。
要模擬實際地震波傳遞情形需要知道實際的地底地質結構,而簡單的分層速度模型方便執行及取得但無法精準的模擬實際的地震波傳遞情形。因此本研究提出使用簡單的速度模型及過去地震歷史資料的學習來建地震波傳遞時間模型。此模型可模擬實際地震波傳遞時間跟簡單的速度模型傳遞時間的差值,間接地可模擬實際地質結構跟分層速度模型結構的不同。
另外提出了一個地震定位的演算法,使用地震波傳遞時間模型預測的傳遞時間差值來校正測站觸發時間並定位。實驗結果指出,使用本研究提出的地震定位演算法可以定位出更準確的震央。

The objective of an earthquake locating system is to quickly and accurately locate the hypocenter, which requires correct triggered times of earthquake sensors and a proper velocity model. This work is related to the velocity model with which the traveling time from one point to the other can be derived.
The real velocity model can hardly be obtained due to insufficient knowledge on the actual geological structure. This research proposes to construct through learning a model for the difference of traveling times obtained by (1) a simple one-dimensional velocity model, and (2) the observed traveling time from historical events. With the traveling time residual model, the locating system can obtain a more accurate traveling time by adding the residual returned by the model to the observed traveling time.
An iterative earthquake locating flow that employs the traveling time residual models to calibrate the P wave traveling times is also proposed. Experiment results indicate that the proposed earthquake locating algorithm can improve the locating accuracy.


口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
Contents v
List of Figures viii
List of Charts ix
List of Tables x
Chapter 1 Introduction 1
1.1 Motivation and Objective 1
1.2 Related Research 2
1.2.1 Earthquake Locating Methods 2
1.2.2 Velocity and Traveling Time Model 2
1.2.3 Traveling Time Calibration 3
1.3 Proposed Techniques 3
1.3.1 Contributions 4
Chapter 2 Preliminaries 5
2.1 Traveling Time Calculation 5
2.1.1 Velocity Model 5
2.1.2 Huygens’ Principle 6
2.1.3 Traveling Time Table 7
2.2 Locating Method: Geiger’s Method 8
2.3 Support Vector Machines 9
Chapter 3 Learning-Based Traveling Time Calibration 10
3.1 Basic Concept 10
3.1.1 Traveling Time 11
3.1.2 Traveling Time Calibration 11
3.1.3 Validation of the Proposed Concept 12
3.2 System Overview 14
3.3 Traveling Time Residual Model 15
3.3.1 Training Data and SVM Features 15
3.3.2 Build Different Models 16
3.4 Predicted Residual at Station 27
3.4.1 Northern Region Station: TAP 28
3.4.2 Central Region Station: WCH 28
3.4.3 Southern Region Station: KAU 29
3.4.4 Eastern Region Station: EGA 30
3.4.5 Discussion in Residuals and Stations 31
3.5 Locating Technique 32
3.5.1 Model Selection 33
3.5.2 Convergence Conditions 35
Chapter 4 Experiment Results 36
4.1 Locate Historical Events 36
4.1.1 Experiment Events and Setup 37
4.1.2 Experiment Results 37
4.2 Locate Future Events 39
4.2.1 Experiment Events and Setup 39
4.2.2 Experiment Results 39
4.2.3 Summary on Locating Future Events 41
Chapter 5 Conclusions 42
5.1 Thesis Summary 42
5.2 Future Work 42
Reference 43

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[2]Y. L. Chen, and T. C. Shin, "Study on the Earthquake Location of 3-D velocity Structure in the Taiwan Area," Meteoritical Bulletin, vol. 42, pp. 135-169, 1998.
[3]E. A. Robinson, and D. Clark, "Basic Seismology 13—Huygens'' principle," The Leading Edge, vol. 25(10), pp. 1252-1255, 2006.
[4]M. Ge, "Analysis of Source Location Algorithms Part I: Overview and Non-iterative Methods," Journal of Acoustic Emission, vol. 21, pp. 14-28, 2003.
[5]M. Ge, "Analysis of Source Location Algorithms Part II: Iterative Methods," Journal of Acoustic Emission, vol. 21(1), pp. 29-51, 2003.
[6]H. Y. Lee, "A Self-Calibrating Earthquake Locator," M.S. thesis, National Taiwan University, 2014.
[7]C. Zhang, C. Frogner, M. Araya-Polo, and D. Hohl, "Machine-learning Based Automated Fault Detection in Seismic Traces," EAGE Conference and Exhibition, 2014.
[8]T. Evgeniou, M. Pontil, and T. Poggio, "Regularization networks and support vector machines," Advances in computational mathematics, vol. 13(1), pp. 1-50, 2000.
[9]Y. M. Wu, and H. Kanamori, "Experiment on an onsite early warning method for the Taiwan early warning system," Bulletin of the Seismological Society of America, vol. 95(1), pp. 347-353, 2005. 
[10]Y. M. Wu, D. Y. Chen, T. L. Lin, C. Y. Hsieh, T. L. Chin, W. Y. Chang, W. S. Li, and S. H. Ker, "A High‐Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors," Seismological Research Letters, vol. 84(6), pp. 1048-1054, 2013.
[11]C. C. Chang, and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2(3), article no. 27, 2011.
[12]N. C. Hsiao, Y. M. Wu, T. C. Shin, L. Zhao, and T. L. Teng, "Development of earthquake early warning system in Taiwan," Geophysical research letters, vol. 36(5), 2009.
[13]R. Allen, "Seconds before the big one," Scientific American, vol. 304(4), pp. 74-79, 2011.


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