| 研究生: |
李柏逸 Li, Po-I |
|---|---|
| 論文名稱: |
臺灣2006至2022年地表同震位移場目錄之建置 A unified geodetic data-based earthquake catalog for the Taiwan mountain belt from 2006 to 2022 |
| 指導教授: |
景國恩
Ching, Kuo-En |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 測量及空間資訊學系 Department of Geomatics |
| 論文出版年: | 2022 |
| 畢業學年度: | 111 |
| 語文別: | 英文 |
| 論文頁數: | 215 |
| 中文關鍵詞: | GNSS連續站 、GNSS移動站 、精密水準測量 、時間序列 、同震位移場 、同震震源模型 |
| 外文關鍵詞: | continuous GNSS station, campaign-mode GNSS station, precise leveling survey, time series, coseismic displacement, coseismic source model |
| 相關次數: | 點閱:192 下載:31 |
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利用GNSS連續站資料研究臺灣2002年以來發生大地震事件的地表同震位移場成果已相當完整,然而GNSS連續站資料存在一些限制,由於測站數的不足資料所能提供的空間解析力仍是有限,對於垂直位移場的成果可靠度也有待提升。本研究因此利用中央地質調查所施測的333站GNSS移動站和19條精密水準測線的資料,來提升2006年以來主要地震事件的同震位移場空間解析力與垂直位移場可靠度。這些事件包含2006年ML 7.0屏東外海地震、2010年ML 6.4甲仙地震、2013年ML 6.4瑞穗地震、2013年3月ML 6.2南投地震、2013年6月ML 6.5南投地震、2016年ML 6.6美濃地震、2018年ML 6.2花蓮地震及2022年ML 6.8玉里地震。本研究利用GNSS移動站和精密水準之坐標時間序列分析來計算地表同震位移場,接著引用各地震已發表的GNSS連續站同震位移場成果,進行GNSS連續站、GNSS移動站和精密水準同震位移場之型態比對,確認各成果相互間之一致性,並獲得各地震完整之同震位移場,隨後透過反演同震震源模型來推估各地震之滑移分布。雖然一般認為GNSS移動站之高程坐標精度為平面坐標精度的三倍,但經由不同資料間的比對,本研究發現GNSS移動站之垂直同震位移場仍可以可靠地反映地震當下的垂直同震位移型態。因為臺灣造山帶的活動斷層以逆斷層為主,因此精密水準資料能改善斷層模型傾角的解析力,成為提供研究此構造的重要資訊。另外,本研究也發現位於臺灣西南部的未知活動背斜構造,其可能與泥貫入體有關。本研究所提供的斷層模型能幫助我們了解地震當下斷層的運動方式,並推估未來地震發生的潛勢及評估能造成的災害。
The coseismic displacements of major earthquakes in Taiwan obtained by continuous GNSS (cGNSS) were already been provided since 1999. However, cGNSS has some limitations, like the spatial resolution is not enough because of the lack of stations, and the poor accuracy in the vertical component. Therefore, in this study, I collected and calculated the geodetic data from 333 campaign-mode GNSS (sGNSS) stations and 19 precise leveling routes to enhance the spatial resolution and reliability of vertical component of coseismic displacement fields. The coordinate time series analysis was used to derive the coseismic displacements of each earthquake from the sGNSS and precise leveling data by using the least square method. I provided a geodetic data-based earthquake catalog of Taiwan from 2006 to 2022, which included 2006 ML 7.0 Pingtung offshore earthquake, 2010 ML 6.4 Jiashian earthquake, March 2013 ML 6.2 Nantou earthquake, June 2013 ML 6.5 Nantou earthquake, 2013 ML 6.4 Ruisui earthquake, 2016 ML 6.6 Meinong earthquake, 2018 ML 6.2 Hualien earthquake, and 2022 ML 6.8 Yuli earthquake. Then I collected and compared published cGNSS coseismic displacement fields with the sGNSS and precise leveling displacement fields, validating that all patterns of coseismic results are similar, and I calculated the slip distribution on the fault by coseismic source model inversion. For the sGNSS data, although the precision of vertical component is generally three times larger than the horizontal component, the vertical component of sGNSS stations coseismic displacements are still reliable because of the comparison from leveling and cGNSS data. Because most active structures of the Taiwan mountain belt are reverse faults, consequently the precise leveling measurements could improve the resolution of the modeled fault dip angle, which provide important information of the kinematic analysis for these structures. Also, unknown reactivated anticlinal structures in SW Taiwan were discovered in this study, which may be associated with the active mud diapirs. The models could help us understand the type of faulting during the earthquake, estimate the potential of the next earthquake, and evaluate the seismic hazard in the future.
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