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系統識別號 U0026-1508201615112700
論文名稱(中文) 走時序列定位法及波形疊加法應用於臺灣線上即時區域地震預警陣列
論文名稱(英文) On-line applications of the travel-time sequence method for locating earthquake and the waveform stacking method for the local EEW arrays in Taiwan
校院名稱 成功大學
系所名稱(中) 地球科學系
系所名稱(英) Department of Earth Sciences
學年度 104
學期 2
出版年 105
研究生(中文) 黃瑞樺
研究生(英文) Ruei-Hua Huang
電子信箱 prince02050830@gmail.com
學號 L46031120
學位類別 碩士
語文別 中文
論文頁數 194頁
口試委員 指導教授-樂鍇‧祿璞崚岸
口試委員-饒瑞鈞
口試委員-李恩瑞
口試委員-吳逸民
口試委員-陳達毅
中文關鍵字 地震預警  地震減災  地震預警陣列  即時地震定位 
英文關鍵字 Earthquake early warning arrays  The waveform stacking method  The travel-time sequence method 
學科別分類
中文摘要 台灣位於歐亞板塊與菲律賓海板塊交界的地震活躍帶,地震災害一直都是台灣人民的夢魘,對於如何有效地防災減災是我們必須努力的方向。地震預警是防救災科技的其中之一,探討如何在震後數秒內提供地震參數,讓人民或政府有準備防災,目前已有技術能夠線上處理地震資訊並發布警報,但由於此技術尚新,還有進步的空間,因此本研究的重點在於利用離線的地震預警參數計算方式應用到線上即時系統中,增加線上求得的地震參數之精準度或是提供另外的地震資訊當作地震預警參考。
  Palert是一套以地震預警為目的設計的儀器,為低價位MEMS型加速度計且安裝於建物上,其最大優勢在於測站數量多且分布廣,但其訊噪比(singal-to-noise ratio, S/N)低,易產生誤報。為了增加S/N,本研究採用區域地震預警陣列模式,以波形疊加法提升訊號品質並使用適用於Palert地震預警網的規模預估公式來計算地震大小。Palert地震預警系統使用的線上處理軟件為Earthworm,此為美國地質調查所公開分享的軟體,為了日後能將本研究結果應用在現有的Palert地震預警系統架構上,本研究將選用此軟件為線上處理軟件。以美濃地震測試的離線結果顯示,S/N有提升且地震規模有較趨近於氣象局公布的芮氏規模6.6,另外出報告時間擴建模組前後最多只相差0.2秒的時間。
在地震定位方面,本研究使用走時序列定位法應用在區域陣列中。此法的優點有三,其一在於它以三維速度構造模型及虛擬曲折波線追跡法去計算走時,較符合地震波傳遞的真實情況;其二,走時計算是在地震事件前就處理完,地震發生時,只需透過簡單的四則運算去搜尋震源,方便又快速;其三,當震源位於測站分布網內,定位越精準。但由於部分的測站受到外在因素影響而壞站或測站波形品質不良造成中心在處理訊號時不採用,使得地震報告中缺站的情形嚴重,或是P波到時自動解不精確,以上情況對於此定位法影響很重,易造成定位誤差被放大,故本研究提出兩種修正法來降低定位上誤差,分別為:
1. 取前十名測站依序的定位結果平均
2. 將排名差距縮減
在離線測試中,使用了18個規模大於5的地震,Palert地震預警系統計算出的震央及深度相較於中央氣象局地震目錄之定位結果,島內地震震央平均差值11公里,深度平均誤差8公里,海外地震震央平均差值17公里,深度平均誤差13公里,全部地震事件震央平均差值14公里,深度平均差值11公里;本研究計算出的震央及深度相較於中央氣象局地震目錄之定位結果,使用格點搜尋法,島內地震震央平均差值10公里,深度平均誤差9公里,海外地震震央平均差值27公里,深度平均誤差13公里,全部地震事件震央平均差值18公里,深度平均差值11公里;經由第一種修正法後,島內地震震央平均差值8公里,深度平均誤差7公里,海外地震震央平均差值23公里,深度平均誤差14公里,全部地震事件震央平均差值16公里,深度平均差值11公里;經由第二種修正法後,島內地震震央平均差值9公里,深度平均誤差8公里,海外地震震央平均差值20公里,深度平均誤差11公里,全部地震事件震央平均差值15公里,深度9公里。
  在本研究中此定位法尚未線上化,本研究目的在於測試此定位法是否適用於Palert地震預警系統。離線測試結果顯示,在島內地震此定位法較優,而海外地震則反之,在運算時間上兩者差異不大;此定位法未來線上化後,可與現行的定位法互相對比,提供Palert地震預警系統更多可參考的定位結果。
英文摘要 INTRODUCTION
Taiwan is located on the west of Circum-Pacific Seismic Zone. How to reduce earthquake damages is a very important work. EEW is one of method for seismic hazard mitigation. Quick estimation on magnitude and earthquake location after an earthquake occurring are two main tasks for EEW system. Palert is a new, MEMS-type and low-cost accelerometer and has installed more than 500 stations in Taiwan since June 2012. This high density of seismic station is benefit to EEW system, but its relatively low signal-to-noise ratio (S/N) would reduce the accuracy of estimation. Based on the result from Tsai et al. (2015), we create a new module for waveform stacking using Earthworm platform, which is free software from USGS. The result of this study suggests that real-time, on-line array stacking can enhance the S/N ratio and get more accurate estimated magnitude.
In earthquake locating respect, we apply the travel-time sequence method (Tsai et al., 2015) to Palert EEW network. The travel-time sequence method uses 3-D velocity model and 3-D ray tracing technique to compute the travel time. In this study, we propose two ways to adjust the grid searching method. One is calculating the average of the points which are found from the first one to the first ten triggered stations. Another is cutting down the difference between the database and the real travel-time sequence of stations. The results show the feasibility of the method and provide additional locating earthquake method to the Palert EEW system.

Method and Data Analysis
(1) The new local on-line EEW arrays
Earthworm is a free software from USGS and used to receive and manage real-time signals recorded by the field Palert stations. According to the result from Tsai et al. (2015), relatively low S/N ratio of Palert device would reduce the accuracy of estimation and the waveform stacking method can increase S/N ratio. Fig. 1 illustrates the Earthworm configuration of the Palert system. The shared memories are indicated by four circles. Three modules named PICK_EEW, TCPD, and DCSN were created for managing P-wave phase picking (PICK_EEW), trigger associations (PICK_EEW), hypocenter locations (TCPD), magnitude estimations (TCPD), and alert filtering prior to broadcasting (DCSN) by Chen et al. (2015a and b) (Fig. 1). In this study, we create a new module for cross-correlation and waveform stacking as shown in fig.2. To test the new structure of this study, we reply the 20160206 Meinong earthquake to the Palert EEW system and evaluate the performance on magnitude estimation.
Fig.1 The mode of operation in Earthworm
Fig.2 The mode of operation in Earthworm

(2) The travel-time sequence method for locating earthquake for the local EEW arrays in Taiwan
The high density of Palert in Taiwan is very useful in EEW. In this study, we apply the travel-time sequence method (Tsai et al., 2015) on it. This method use 3-D velocity model and pseudo-bending method to compute the travel time and compare the station sequence of event with database to locate the hypocenter quickly. However, the uncertainty of P-wave picking due to low S/N ratio may cause the location error. In this study, we propose two ways to modify the grid searching method. For increasing the truly point ratio, we calculate the average of the points which are found from the first one to the first ten triggered stations. Another way is shorten the difference between the station sequence of event and database to reduce the effect of above cases.
Result and Discussion
(1) The new local on-line EEW arrays
The quality criteria for picking are described in the study of Chen et al. (2015a and b). The report from TCPD has 21 stations from 15 arrays in SW Taiwan. The result show that the S/N ratio after the stacking and the accuracy of estimated Mpd are better than before but the reporting time difference between the two methods is not obvious. It’s just 0.09±0.044 s. This result is enough to support that the new method is more suitable for Palert EEW system.

(2) The travel-time sequence method for earthquake locating for the local EEW arrays in Taiwan
After self-simulate of database, we decide to use 10 stations to locate events. In this study, we choose events from 2013 to 2015 with ML > 5. The result shows using two modified methods can get more accurate earthquake location. In inland events, the location error of Palert EEW system is 11 km and the best of this study is 8 km. In offshore events, the location error of Palert EEW system is 17 km and the best of this study is 20 km. The result show that if we can combine two types of location methods, it may provide a reliable, precise and stable earthquake location result.

Conclusion
(1) The new local on-line EEW arrays
1. We have established a new hybrid-type of EEW.
2. This hybrid-type of EEW based on local array can make the Palert EEW system more usable.
3. The array-based EEW system can provide more accurate estimate on earthquake magnitude.

(2) The travel-time sequence method for earthquake locating for the local EEW arrays in Taiwan
1. The two modified grid searching method can effectively improve the coverage problem of Palert.
2. This method is more suitable for a region with a more complicated velocity structure.
3. The average arrival time of the tenth triggered station for all events is of about 8 seconds, which has the potentiality for Palert EEW system usage.
論文目次 目錄
論文說明 II
摘要 III
Extended Abstract V
誌謝 IX
目錄 XI
圖目錄 XVI
表目錄 XX
第一章、緒論
1.1 前言 1
1.2 各國地震預警歷史與發展 3
1.2.1 日本的地震預警系統 4
1.2.2 美國的地震預警系統 7
1.2.3 墨西哥的地震預警系統 9
1.2.4 臺灣的地震預警系統 11
1.3 研究動機及目的 16
第二章、地震預警原理
2.1 地震預警基本概念 18
2.2 地震預警模式 21
2.3 地震預警之規模預估法 23
2.3.1 ML10法 23
2.3.2 P波週期預估法(τ_p^max法、τ_c法) 24
2.3.3 P波振幅預估法(Pd法) 26
2.3.4 最大強地動加速度預估法(MPGA法) 27
2.4 地震預警之地震定位法 28
2.4.1 傳統地震定位 28
2.4.2 單站定位 29
2.4.3 測站幾何分布定位 31
2.4.4 格點搜尋定位 32
2.4.5 走時序列定位 32
第三章、研究方法
3.1 第一部分-波形疊加法應用於線上即時區域地震預警陣列 39
3.1.1 基礎理論 39
3.1.2 線上系統說明 42
3.1.3 擴建基本架構 43
3.1.4 調整現有模組 48
3.1.5 測試 48
3.2 第二部分-走時序列定位法應用於
線上即時區域地震預警陣列 49
3.2.1 前人研究 49
3.2.2 測站走時資料庫建立 52
3.2.3 新格點搜尋法 53
第四章、研究成果
4.1 第一部分-波形疊加法應用於線上即時區域地震預警陣列 55
4.1.1 測試結果 55
4.2 第二部分-走時序列定位法應用於
線上即時區域地震預警陣列 61
4.2.1 資料庫自身模擬測試(Self-simulation test) 61
4.2.2 地震定位結果 63
第五章、討論
5.1 第一部分-波形疊加法應用於
線上即時區域地震預警陣列結果討論 70
5.1.1 運算結果說明與分析 70
5.1.2 擴建模組對系統運算時效討論 71
5.2 第二部分-走時序列定位法應用於
線上即時區域地震預警陣列結果討論 72
5.2.1 自身模擬結果分析 72
5.2.2 定位結果與定位時效分析 74
第六章、結論與建議 80
參考文獻 82
附錄一:Palert地震預警系統測站分布 92
附錄二:陣列之測站分區列表 110
附錄三:各地震事件定位結果 128
附錄四:常用Earthworm指令 136
附錄五:Earthworm新模組程式碼(C語言) 137
附錄六:建立走時資料庫程式碼(FORTRAN 95) 164
附錄七:格點搜尋修正法A程式碼(FORTRAN 95) 188
附錄八:格點搜尋修正法B程式碼(FORTRAN 95) 192
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