應用XGBoost模型建立台灣地殼強地震動衰減式

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：72

、訪客IP：18.217.237.169

姓名

張智宇(Chih-Yu Chang) 查詢紙本館藏

畢業系所

地球科學學系

論文名稱

應用XGBoost模型建立台灣地殼強地震動衰減式
(Applying XGBoost to establish the ground motion model in Taiwan)

相關論文

★ 開發深度學習技術的台灣轉換器震動警報模型（TT-SAM）及其應用

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)

本研究利用XGBoost模型，結合台灣地震危害高階模型建置（TWSSHAC）計畫蒐集台灣強震觀測網（TSMIP）地殼地震紀錄所得之Flatfile資料庫，分別針對PGA、PGV、以及反應頻譜建立地殼地震模型（GMM）。衡量模型性能時，本研究特別關注殘差標準差和R²分數兩項指標，及透過距離響應、反應頻譜和殘差分佈等多重分析手段，深入評估模型的有效性與合理性。此外，研究方法著重於應用可解釋AI工具SHAP（SHapley Additive exPlanations）指數來揭示不同預測因子在本研究模型中的重要性。此方法不僅闡明了各種因素對強地動的影響，更強調模型的可解釋性，滿足傳統工程地震學對於模型的合理性與可解釋性的需求。
考慮到地震數據集的不均衡問題，特別是在較大規模事件和較短距離的情況下，採用SMOGN方法進行數據增強。這種方法有效平衡數據集，從而使模型在預測大規模地震事件方面具有更好的學習能力。本研究亦評估模型在地震危害分析中的可用性，採用地震危害分析軟體Openquake，納入本研究強地動衰檢模型做地震動模擬。將機器學習技術納入GMM的發展，標誌著台灣地震危害分析的重要進展。這項研究可能對地震準備策略、基礎設施韌性規劃和公共安全協議產生重大影響，展示了機器學習在地震科學中的應用潛力。

摘要(英)

This study utilizes the XGBoost model integrated with the Flatfile database, derived from seismic records of the Taiwan Strong Motion Instrumentation Program (TSMIP) and archived by the Taiwan Seismic Senior Hazard Analysis Committee (TWSSHAC) project, to establish crustal seismic models (GMM) for PGA, PGV, and spectral acceleration. When evaluating the performance of these models, particular attention is paid to the standard deviation of residuals and the R² score, as well as multiple analysis methods such as distance response, response spectra, and residual distribution to thoroughly assess the effectiveness and rationality of the models. Additionally, a key aspect of the methodology is the application of SHAP (SHapley Additive exPlanations) values to reveal the importance of different predictors within our model. This approach not only clarifies the impact of various factors on strong ground motion but also highlights the interpretability of the model, addressing the traditional demands of engineering seismology for model rationality and explainability.
Considering the imbalance in the earthquake dataset, especially in cases of larger-scale events and shorter distances, the SMOGN method is used for data augmentation. This method effectively balances the dataset, thereby enhancing the model′s ability to learn from rare but significant large-scale seismic events. The study also assesses the applicability of the model in seismic hazard analysis using the seismic hazard analysis software, Openquake, incorporating the strong motion attenuation model proposed in this study for seismic motion simulation. The incorporation of machine learning techniques into the development of GMM marks a significant step forward in the advancement of seismic hazard analysis in Taiwan. This research could have a substantial impact on earthquake preparedness strategies, infrastructure resilience planning, and public safety protocols, demonstrating the potential of machine learning in earthquake science.

關鍵字(中)

★ 地震動衰減式
★ 機器學習
★ 地震危害分析
★ 殘差分析

關鍵字(英)

★ Ground motion model
★ machine learning
★ seismic hazard
★ residual analysis

論文目次

第一章緒論 1
1-1 研究動機與目的 1
1-2 文獻回顧 2
1-2-1 傳統方法建立強地震動模型相關研究 3
1-2-2 機器學習建立強地震動模型相關研究 4
1-3 研究流程與內容 5
第二章資料處理與分析 9
2-1 強震資料蒐集 9
2-2 強震資料參數挑選 10
2-2-1規模參數 10
2-2-2 距離參數 10
2-2-3 場址參數 11
2-2-4 震源機制參數 12
2-3 強震資料分析 12
第三章研究方法 22
3-1 強地動衰檢模型背景 22
3-2 XGBoost模型簡介 22
3-3 強地動模型建立方法 24
第四章台灣地殼地震地動預估模型 32
4-1 地動預估模型評判依據 32
4-2 地動預估模型結果與分析 33
4-3 誤差項拆解 34
4-4 國內外地動預估式、預估模型比較 35
第五章討論 51
5-1 強地動預估式潛在問題與改善方法 51
5-2 增強模型於強震之預測 52
5-3 可解釋強地震動模型 55
5-3-1 模型之全域解釋 56
5-3-2 模型之局部解釋 57
5-4 地震危害分析之應用 58
5-4-1 地震危害分析概述 58
5-4-2 地震危害分析軟體與應用結果 59
第六章結論與建議 74
參考文獻 76

參考文獻

Abrahamson NA, Silva WJ, Kamai R. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions. Earthquake Spectra. 2014;30(3):1025-1055. doi:10.1193/070913EQS198M
Abrahamson, N.A. and W.J. Silva (1997). Empirical response spectral attenuation relations for shallow crustal earthquakes. Seismological Research Letters 68, 94-127. Abrahamson, N.A. and K.M. Shedlock (1997). Overview of attenuation relations special issue. Seismological Research Letters 68, 9-23.
Abrahamson, N.A. and K.M. Shedlock (1997). AOverview.@ Seism. Research Lett., 68(1), 9-23.
Anderson, J. G., & Brune, J. N. (1999). Probabilistic seismic hazard analysis without the ergodic assumption. Seismological Research Letters, 70(1), 19-28.
Anderson, J. G., Lee, Y. J., Zeng, Y. H., and Day, S. (1996). Control of strong motion by the upper 30 meters. Bulletin of the Seismological Society of America, 86(6), 1749-1759.
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019, July). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631).
Boore, D. M., & Atkinson, G. M. (2008). Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthquake spectra, 24(1), 99-138.
Branco, P., Torgo, L., & Ribeiro, R. P. (2017, October). SMOGN: a pre-processing approach for imbalanced regression. In First international workshop on learning with imbalanced domains: Theory and applications (pp. 36-50). PMLR.
Campbell, K. W., & Bozorgnia, Y. (2014). NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra. Earthquake Spectra, 30(3), 1087-1115.
Chao, S. H., Chiou, B., Hsu, C. C., & Lin, P. S. (2020). A horizontal ground-motion model for crustal and subduction earthquakes in Taiwan. Earthquake Spectra, 36(2), 463-506.
Chan, C. H., Ma, K. F., Shyu, J. B. H., Lee, Y. T., Wang, Y. J., Gao, J. C., ... & Rau, R. J. (2020). Probabilistic seismic hazard assessment for Taiwan: TEM PSHA2020. Earthquake Spectra, 36(1_suppl), 137-159.
Chiou BSJ, Youngs RR (2008) An NGA model for the average horizontal component of peak ground motion and response spectra. Earthquake Spectra 24(1): 173–215.
Cornell, C. A., “Engineeing seismic risk analysis.” Bulletin of the Seismological Society of America, 58, 1583-1606, 1968
Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
Dang, H., Wang, Z., Zhao, D., Wang, X., Li, Z., Wei, D., & Wang, J. (2024). Ground motion prediction model for shallow crustal earthquakes in Japan based on XGBoost with Bayesian optimization. Soil Dynamics and Earthquake Engineering, 177, 108391.
Dhanya, J., & Raghukanth, S. T. G. (2018). Ground motion prediction model using artificial neural network. Pure and Applied Geophysics, 175, 1035-1064.
Hu, J., & Zhang, H. (2022). Support vector machine method for developing ground motion models for earthquakes in western part of China. Journal of Earthquake Engineering, 26(11), 5679-5694.
Khosravikia, F., & Clayton, P. (2021). Machine learning in ground motion prediction. Computers & Geosciences, 148, 104700.
Kuo, C. H., Wen, K. L., Hsieh, H. H., Lin, C. M., Chang, T. M., & Kuo, K. W. （2012）. Site classification and Vs30 estimation of free-field TSMIP stations using the logging data of EGDT. Engineering Geology, 129-130, 68-75.
Kuo, C. H., Chen, C. T., Lin, C. M., Wen, K. L., Huang, J. Y., & Chang, S. C. （2016）. S-wave velocity structure and site effect parameters derived from microtremor arrays in the Western Plain of Taiwan. Journal of Asian Earth Sciences, 128, 27-41.
Kwok, O. L. A., Stewart, J. P., Kwak, D. Y., & Sun, P. L. （2018）. Taiwan-specific model for V s30 prediction considering between-proxy correlations. Earthquake Spectra, 34（4）, 1973-1993.
Lin, P. S., Lee, C. T., Cheng, C. T., & Sung, C. H. (2011). Response spectral attenuation relations for shallow crustal earthquakes in Taiwan. Engineering Geology, 121(3-4), 150-164.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
Lin CM, Kuo CH, Huang JY, Hsieh HH, Si CC and Wen KL （2017） Shallow shear-wave velocity structures of TSMIP stations in Taiwan. NCREE Report, No. NCREE-17-005. Taipei: National Center for Research on Earthquake Engineering.
Mohraz, B. (1976). A study of earthquake response spectra for different geological conditions. Bulletin of the Seismological Society of America, 66(3), 915-935.
Pagani, M., Monelli, D., Weatherill, G., Danciu, L., Crowley, H., Silva, V., ... & Vigano, D. (2014). OpenQuake engine: An open hazard (and risk) software for the global earthquake model. Seismological Research Letters, 85(3), 692-702.
Phung, V. B., Loh, C. H., Chao, S. H., Chiou, B. S., & Huang, B. S. (2020). Ground motion prediction equation for crustal earthquakes in Taiwan. Earthquake Spectra, 36(4), 2129-2164.
Seed, H. B., Ugas, C., & Lysmer, J. (1976). Site-dependent spectra for earthquake-resistant design. Bulletin of the Seismological society of America, 66(1), 221-243.
Seo, H., Kim, J., & Kim, B. (2022). Machine‐learning‐based surface ground‐motion prediction models for South Korea with low‐to‐moderate seismicity. Bulletin of the Seismological Society of America, 112(3), 1549-1564.
Shakal, A. F., Huang, M. J., & Graizer, V. M. （2004, May）. CSMIP Strong motion data processing. In Proc. invitational workshop on strong motion record processing.
Wang, Y. J., Lee, Y. T., Chan, C. H., & Ma, K. F. (2016). An investigation of the reliability of the Taiwan Earthquake Model PSHA2015. Seismological Research Letters, 87(6), 1287-1298.
Youngs, R. R., Abrahamson, N., Makdisi, F. I., & Sadigh, K. (1995). Magnitude-dependent variance of peak ground acceleration. Bulletin of the Seismological Society of America, 85(4), 1161-1176.
陳冠朋。「以XGBOOST演算法探討台北地區地震動預測研究」。碩士論文，國立臺灣大學土木工程學研究所，2022。https://hdl.handle.net/11296/udzce3。
https://gdms.cwa.gov.tw/ 中央氣象署-台灣地震與地球物理資料管理系統
https://sshac.ncree.org.tw/ TWSSHAC計畫專案網站

指導教授

詹忠翰馬國鳳(Chung-Han Chan Kuo-Fong Ma)

審核日期

2024-6-11

推文