以愛氏震度分區建立地震誘發山崩潛感羅吉斯回歸 模型以及地震後山崩潛感近及時分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：82

、訪客IP：3.142.40.43

姓名

沈楷庭(Kai-Ting Shen) 查詢紙本館藏

畢業系所

應用地質研究所

論文名稱

以愛氏震度分區建立地震誘發山崩潛感羅吉斯回歸模型以及地震後山崩潛感近及時分析

相關論文

★ 以孔隙比、有效應力與剪力波速關係繪製臺北盆地Vs30分布圖

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

地震造成的損失除了直接導因於地震動外，地震動所引發的山崩也是生命財產損失之重要原因，因此，地震發生後快速且準確地知道哪裡可能已發生山崩，對災後救援或緊急應變對策研擬至關重要。
本研究目標為半自動化近即時繪製山崩潛感圖，盡量減少人工資料處理時間，本研究使用羅吉斯回歸計算潛感值，模型選擇了坡度、坡度粗糙度、地表粗糙度、總曲率、全坡高、愛氏震度、坡向、岩性。本研究依愛氏震度(Arias Intensity)進行資料分區，並根據兩種訓練資料取樣方法設計出兩種模型，以計算不同震度下的羅吉斯回歸模型潛感值，並評估不同震度下先天因子對潛感值之影響程度。
本研究撰寫了半自動化Python程式計算羅吉斯回歸模型潛感值，經過測試可以在1分鐘內建立完山崩潛感模型，並在15分鐘左右繪製出山崩潛感圖。未來希望能結合餘震預測的技術，當主震發生時，先利用已建立好的山崩潛感模型，建立第一版之山崩潛感圖，待取得遙測技術自動判釋取得山崩目錄以及強震站的地震訊號後，再以主震誘發山崩之山崩目錄建立新的模型，配合餘震預測技術，所得到餘震可能造成的震度空間分布，即可預測餘震可能造成的山崩分布，做為主震後救援或緊急應變之重要參考資料。

摘要(英)

The losses caused by earthquakes are not only directly attributable to the seismic shaking but also to the significant contribution of landslides triggered by the seismic activity. Therefore, it is crucial to quickly and accurately determine the locations where landslides may have occurred after an earthquake, in order to develop effective strategies for post-disaster rescue and emergency response.
The objective of this study is to generate semi-automatically near-real-time susceptibility maps for landslides, aiming to minimize manual data processing time. In this study, logistic regression is used to calculate the susceptibility. The model selected the following factors: slope, slope roughness, terrain roughness, total curvature, slope height, Arias Intensity, aspect, and lithology. The data is partitioned based on the Arias Intensity, and two models are designed using two different training data sampling methods to calculate the susceptibility values for different intensities. The study also evaluates the influence of inherent factors on susceptibility values under different intensities.
This study developed a semi-automated Python program to calculate susceptibility. Through testing, it was found that the program can establish a landslide susceptibility model within one minute and generate a susceptibility map in approximately 15 minutes. In the future, the aim is to integrate techniques for aftershock prediction. When a main shock occurs, the established landslide susceptibility model can be used to create an initial version of the susceptibility map. Then, with the automatic interpretation of remote sensing data for landslide inventories and seismic signals from strong motion stations, a new model can be constructed using the landslide catalog induced by the main shock. By combining this with aftershock prediction techniques and the spatial distribution of shaking intensity caused by aftershocks, it will be possible to predict the distribution of landslides that may occur as a result of aftershocks. This information can serve as an important reference material for rescue or emergency response after a main shock.

關鍵字(中)

★ 羅吉斯回歸
★ 山崩潛感圖
★ 愛氏震度
★ 地震誘發山崩

關鍵字(英)

★ Logistic regression
★ Landslide susceptibility map
★ Arias intensity
★ Earthquake-induced landslide

論文目次

摘要 .................................................................................................................... i
Abstract .............................................................................................................. ii
致謝 .................................................................................................................. iv
目錄 ................................................................................................................... v
圖目錄 ............................................................................................................ viii
表目錄 ............................................................................................................... x
第一章緒論 ...................................................................................................... 1
第二章資料與方法 .......................................................................................... 6
2-1 山崩目錄及使用資料 .............................................................................. 6
2-2 羅吉斯回歸(Logistic Regression) ............................................................ 8
2-3 山崩潛感模型訓練資料選取原則 ........................................................ 11
2-3-1 山崩區域排除準則 .......................................................................... 11
2-3-2 非山崩區排除準則 .......................................................................... 11
2-3-3 模型因子之計算方式 ...................................................................... 15
2-4 檢驗羅吉斯回歸模型因子之方法 ........................................................ 22
2-4-1 山崩組與非山崩組因子次數分布圖 ............................................... 22
2-4-2 標準化差異(standardized difference) ............................................... 23
2-4-3 成功率曲線(Success Rate Curve, SRC)與曲線下面積(Area Under
Curve, AUC) .............................................................................................. 23
2-5 模型建立 ................................................................................................ 25
vi
2-5-1 全區建模，全區等比例取樣 ........................................................... 25
2-5-2 利用愛氏震度分區之羅吉斯回歸模型 ........................................... 25
2-6 回歸模型結果之統計分析方法 ............................................................ 29
2-6-1 混淆矩陣(Confusion matrix) ............................................................ 29
2-6-2 成功率曲線(Success Rate Curve, SRC)與曲線下面積(Area Under
Curve, AUC) .............................................................................................. 30
2-7 使用瑞里地震驗證模型 ......................................................................... 32
第三章羅吉斯回歸建模結果及驗證 ............................................................. 33
3-1 羅吉斯回歸因子檢驗結果 ..................................................................... 33
3-2 羅吉斯回歸模型結果 ............................................................................. 37
3-2-1 全區建模，全區等比例取樣建模結果 ........................................... 39
3-2-2 分區建模，全區等比例取樣建模結果 ........................................... 42
3-2-3 分區建模，分區等比例取樣建模結果 ........................................... 46
3-3 羅吉斯回歸模型驗證 ............................................................................. 50
第四章討論 .................................................................................................... 54
4-1 三種不同之羅吉斯回歸模型比較 ......................................................... 54
4-2 取樣比例不同對模型造成的影響 ......................................................... 55
4-3 山崩與坡向因子的關係 ......................................................................... 57
4-4 當愛氏震度等於零之潛感圖 ................................................................. 58
4-5 探討近即時測繪方法與應用 ................................................................. 59
4-5-1 近即時繪製山崩潛感圖之可行性分析與建議 ............................... 59
4-5-2 預測餘震山崩潛感圖之應用 ........................................................... 60
第五章結論 .................................................................................................... 61
參考文獻 ......................................................................................................... 62

參考文獻

李錫堤，張瓊文，2015。地質災害潛感分析。地質，34(4)，52-55。
林繼煒，2018。應用邏輯斯迴歸於崩塌時間與空間預測的探討。國立彰化師範大學地理學系碩士論文。
林淑媛，2003。地形地質均質區劃分與山崩因子探討。國立中央大學應用地質研究所碩士論文。
莊緯璉，2005。運用判別分析進行山崩潛感分析之研究－以臺灣中部國姓地區為例。國立中央大學應用地質研究所碩士論文。
廖軒吾，2000。集集地震誘發之山崩。國立中央大學地球物理研究所碩士論文。
Bird, J.F., and Bommer, J.J. (2004).Earthquake losses due to ground failure. Engineering Geology. 75(2), 147-179.
Chuang, Y.R, Wu, B.S., Liu, H.C., Huang, H.H., and Lu, C.H. (2021). Development of a statistics-based nowcasting model for earthquake-triggered landslides in Taiwan. Engineering Geology. 289, 106177.
Chung, C.F., and Fabbri, A.G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards. 30, 451-472.
Jibson, R.W., Harp, E.L., and Michael, J.A. (2000). A method for producing digital probabilistic seismic landslide hazard maps. Engineering Geology. 58, 271-289.
Keefer, D.K. (1984). Landslides caused by earthquakes. Geological Society of America Bulletin. 95(4), 406-421.
Lee, C.T. (2014). Statistical seismic landslide hazard analysis: An example from Taiwan. Engineering Geology. 182(B), 201-212.
Marano, K.D., Wald, D.J., and Allen, T.I. (2010). Global earthquake casualties due to secondary effects: a quantitative analysis for improving rapid loss analyses. Natural Hazards. 52, 319-328.
Marc, O., Hovius, N., Meunier, P., Gorum, T., and Uchida, T. (2016). A seismologically consistent expression for the total area and volume of earthquake-triggered landsliding. Journal of Geophysical Research: Earth Surface. 121, 640–663.
Nowicki, M.A., Hamburger, M.W., Allstadt, K., Wald, D.J., Robeson, S.M., Tanyas, H., Hearne, M., and Thompson, E.M. (2018). A global empirical model for near real-time assessment of seismically induced landslides. Journal of Geophysical Research: Earth Surface. 123(8), 1835-1859
Parker, R.N., Nicholas J.R., and Hales, T.C. (2017). Spatial prediction of earthquake-induced landslide probability. Nature Hazards Earth System Sciences. 1-29.
Robinson, T.R., Rosser, N.J., Davies, R.H., Wilson, T.M., and Orchiston, C. (2018). Near-real-time modeling of landslide impacts to inform rapid response: an example from the 2016 Kaikōura, New Zealand, Earthquake. Bulletin of the Seismological Society of America. 108(3B), 1665-1682.
Shao, X., and Xu, C. (2022). Earthquake-induced landslides susceptibility assessment: A review of the state-of-the-art. Natural Hazards. 2(3), 172-182.
Wang, L.J., Sawada, K., and Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences. 57(14), 81–92.

指導教授

董家鈞李錫堤(Jia-Jyun Dong Chyi-Tyi Lee)

審核日期

2023-7-24

推文