[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124643-en":3,"doc-seo-124643-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124643,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning in Large and Small Earthquakes - From Rapid Large Earthquake Characterization to Slow Fault Zone Processes - Dissertation","Jiun-Ting Lin’s dissertation integrates machine-learning with traditional seismic analysis to address challenges in large- and small-earthquake research. For large events, it introduces M-LARGE to rapidly predict magnitude using rupture simulations and GNSS data, achieving about 99% accuracy and avoiding saturation limitations. It also uses M-LARGE to infer finite-fault parameters and their evolution for fast ground-motion forecasting. For smaller events, it detects low-frequency earthquakes from noisy waveforms, increasing event counts and improving temporal resolution. The work links fast and slow slip behaviors in the 2018 M7.1 Hawaii earthquake and validates rupture simulations using effective stress to support more accurate hazard assessment.","MACHINE LEARNING IN LARGE AND SMALL EARTHQUAKES: FROM RAPID LARGE EARTHQUAKE CHARACTERIZATION TO SLOW FAULT ZONE  \nPROCESSES  \nby  \nJIUN-TING LIN  \nA DISSERTATION  \nPresented to the Department of Earth Sciences and the Division of Graduate Studies of the University of Oregon in partial fulfillment of the requirements  \nfor the degree of  \nDoctor of Philosophy  \nDecember 2022  \nDISSERTATION APPROVAL PAGE  \nStudent: Jiun-Ting Lin  \nTitle: Machine Learning in Large and Small Earthquakes: from Rapid Large Earthquake Characterization to Slow Fault Zone Processes  \nThis dissertation has been accepted and approved in partial fulfillment of the requirements for the Doctor of Philosophy degree in the Department of Earth Sciences by:  \nAmanda M. Thomas Diego Melgar Valerie J. Sahakian Jake Searcy  \nThien Nguyen  \nChairperson and Advisor Core Member and Advisor  \nCore Member  \nCore Member  \nInstitutional Representative  \nand  \nKrista Chronister Vice Provost for Graduate Studies  \nOriginal approval signatures are on file with the University of Oregon Division of Graduate Studies.  \nDegree awarded December 2022  \n© 2022 Jiun-Ting Lin This work is licensed under a Creative Commons Attribution License  \nDISSERTATION ABSTRACT  \nJiun-Ting Lin  \nDoctor of Philosophy Department of Earth Sciences  \nDecember 2022  \nTitle: Machine Learning in Large and Small Earthquakes: from Rapid Large Earthquake Characterization to Slow Fault Zone Processes  \nThis dissertation summarizes the work of integrating machine-learning and traditional seismic analysis techniques into large and small earthquake problems. Earthquake early warning for large magnitude earthquakes is one of the most challenging problems in seismology. Here I develop an algorithm, called M-LARGE, that harnesses machine-learning, rupture simulations, and GNSS data to rapidly predict magnitude without saturation issue with an accuracy of 99%, outperforming other similar methods. I then show how M-LARGE can predict finite fault parameters and their evolution when rupture unfolds for fast and accurate ground motion forecasting.  \nThis dissertation will demonstrate how machine-learning can be used as a data mining tool to detect small magnitude seismicity buried in noisy waveforms. I will show its application to detect LFEs, a special class of small earthquakes typically occur downdip of the seismogenic zone. The model detects more than five times the number of events than the original catalog in Vancouver Island and can apply to unseen stations, which provides a more flexible way to refine the temporal resolution of subduction zone processes. Finally, I will show how do small and slow earthquakes link to large and fast events and their implication on earthquake hazard assessment. With jointly inverted GNSS, strong  \nmotion, and tsunami data of the 2018 M7.1 Hawaii earthquake, I find that fast slip ruptures into the area previously hosts slow slip. The result is further validated by rupture simulations, where we find that the effective stress can be a factor that exerts a dominant control on the rupture extent. This reinforces the idea that an individual section of fault can  \nhost a variety of distinct slip behaviors, and slow slip should be considered as rupture extent for a more accurate hazard assessment.  \nThis dissertation includes previously published and unpublished co-authored  \nmaterial.  \nCURRICULUM VITAE  \nNAME OF AUTHOR: Jiun-Ting Lin  \nGRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:  \nUniversity of Oregon, Eugene  \nNational Central University, Taiwan  \nNational Central University, Taiwan  \nDEGREES AWARDED:  \nDoctor of Philosophy, Earth Sciences, 2022, University of Oregon Master of Science, Geophysics, 2014, National Central University Bachelor of Science, Earth Sciences, 2012, National Central University  \nAREAS OF SPECIAL INTEREST:  \nSeismology  \nMachine Learning  \nData Science  \nInverse Problem  \nPROFESSIONAL EXPERIENCE:  \nGraduate Employee, University of Oregon, 2017–2022 Intern, La","cbCaipZTsyR9gk7V","https://ap.wps.com/l/cbCaipZTsyR9gk7V","pdf",23738102,1,145,"English","en",105,"# Dissertation Abstract\n## Large-earthquake magnitude prediction with M-LARGE\n## Rapid finite-fault parameter forecasting\n## Small-earthquake detection of LFEs\n## Linking fast/slow earthquakes and hazard implications","[{\"question\":\"What does M-LARGE do in the dissertation?\",\"answer\":\"M-LARGE uses machine learning with rupture simulations and GNSS data to rapidly predict earthquake magnitude, avoiding saturation issues, and supports accurate forecasting.\"},{\"question\":\"How is machine learning used for small earthquakes?\",\"answer\":\"The dissertation shows a data-mining approach to detect low-frequency earthquakes buried in noisy waveforms, improving event detection compared with an original catalog.\"},{\"question\":\"How do small and slow earthquakes relate to large and fast events in the findings?\",\"answer\":\"Jointly inverted GNSS, strong-motion, and tsunami data for the 2018 M7.1 Hawaii earthquake indicate that fast slip can rupture into areas previously hosting slow slip, with rupture simulations suggesting effective stress governs rupture extent.\"}]","Machine Learning in Large and Small Earthquakes - 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