[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125216-en":3,"doc-seo-125216-105":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},125216,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Improving Earthquake Rapid Response and Early Warning Performance with Geodesy and Machine Learning","Earthquake rapid response and early warning accuracy depend on characterizing events quickly and reliably while and immediately after they occur. This dissertation investigates how borehole strainmeters can distinguish early earthquakes by magnitude, finding limited deterministic differences between large and small events at their onset. It presents machine-learning methods that improve early-warning performance for large earthquakes by separating seismic signal-bearing GNSS waveforms from high-noise records, reducing low-quality inputs to magnitude estimators. To support global response, it also develops a model that estimates earthquake magnitudes consistently across locations and tectonic settings.","Improving Earthquake Rapid Response and Early Warning Performance with Geodesy  \nand Machine Learning  \nby  \nSydney N. Dybing  \nA dissertation accepted and approved in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nin Earth Sciences  \nDissertation Committee:  \nDiego Melgar, Chair  \nAmanda Thomas, Core Member  \nValerie Sahakian, Core Member  \nDare Baldwin, Core Member  \nBen Farr, Institutional Representative  \nUniversity of Oregon  \nFall 2024  \n© 2024 Sydney N. Dybing  \nThis work is openly licensed via CC BY 4.0. To view a copy of this license, visit [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) .  \nDISSERTATION ABSTRACT  \nSydney N. Dybing  \nDoctor of Philosophy in Earth Sciences  \nTitle: Improving Earthquake Rapid Response and Early Warning Performance with Geodesy and Machine Learning  \nThis dissertation focuses on the work I have undertaken to investigate how quickly and accurately we can characterize earthquakes while and immediately after they occur, and potential ways to improve the systems we use for this. I focus on how we can use the modern technology we have for Earth monitoring as well as modern data processing and analysis techniques such as machine learning to improve the capabilities of the systems we rely on for earthquake disaster response. In this dissertation I present an exploration into the question of how early earthquakes are distinguishable by magnitude using borehole strainmeters, where we found that earthquakes do not appear to be strongly deterministic (i.e., large earthquakes are not inherently different from small earthquakes in their beginning stages) . This has implications for how long it takes to accurately determine the magnitude of an earthquake, particularly for large events which rupture over longer periods of time. I then discuss our development of a machine learning algorithm for improving the performance of earthquake early warning systems for large earthquakes. This algorithm allows for discrimination between noisy GNSS waveforms which do actually contain seismic waves from earthquakes and those which do not, which enables us to reduce the amount of high-noise/low quality data that enters algorithms which determine the magnitude of such earthquakes. While earthquake early warning systems tend to operate only over specific regions such as the U.S. West Coast, organizations such as the USGS’s National Earthquake Information Center also must rapidly publish information such as magnitude about worldwide earthquakes to aid in response efforts. However, magnitude estimation across a large range of earthquake sizesand tectonic settings is technically difficult. To help streamline this process, we developed another machine learning model which allows for the estimation of earthquake magnitudes uniformly for all locations and tectonic settings worldwide, which is also presented in this dissertation. Six multiframe animations with more figures from this chapter are included as supplemental video files.  \nThis dissertation includes previously published and unpublished co-authored material.  \nCURRICULUM VITAE  \nNAME OF AUTHOR: Sydney N. Dybing  \nGRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:  \nUniversity of Oregon, Eugene, OR, USA  \nWashington University in St. Louis, St. Louis, MO, USA.  \nDEGREES AWARDED:  \nDoctor of Philosophy in Earth Sciences, 2024, University of Oregon  \nBachelor of Arts in Geophysics, 2019, Washington University in St. Louis  \nAREAS OF SPECIAL INTEREST:  \nSeismology  \nGeodesy  \nMachine Learning  \nEarthquake Early Warning  \nPROFESSIONAL EXPERIENCE:  \nGraduate Employee, University of Oregon Department of Earth Sciences, 2019-2024 Pathways Intern, U.S. Geological Survey Geologic Hazards Science Center, 2022  \nUndergraduate Research Assistant, Washington University Department of Earth and Planetary Sciences, 2017-2019  \nIncorporated Research Institutions for Seismology Internship, U.S. Geological Sur","cbCaigegOtyZbJ2m","https://ap.wps.com/l/cbCaigegOtyZbJ2m","pdf",18312899,1,165,"English","en",105,"# Dissertation Abstract\n## Research focus and motivation\n## Early earthquake magnitude distinguishability\n## Machine learning for early warning performance\n## Global magnitude estimation across regions\n## Additional materials and prior publications","[{\"question\":\"What is the dissertation’s main goal regarding earthquake characterization?\",\"answer\":\"To determine how quickly and accurately earthquakes can be characterized while and immediately after they occur, and to identify ways to improve the systems used for earthquake disaster response.\"},{\"question\":\"What do borehole strainmeter results suggest about early earthquakes by magnitude?\",\"answer\":\"Early earthquakes are not strongly deterministic, meaning large and small earthquakes do not appear inherently different at the start of their rupture processes.\"},{\"question\":\"How do the machine-learning models improve early warning and magnitude estimation?\",\"answer\":\"One algorithm discriminates between GNSS waveforms that contain seismic waves and those dominated by noise, reducing low-quality inputs for magnitude determination. A second model provides uniform magnitude estimation across global locations and tectonic settings.\"}]","Improving Earthquake Rapid Response and Early Warning Performance with Geodesy and Machine Learning | PDF",1785897541,416,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improving-earthquake-rapid-response-and-early-warning-performance-with-geodesy-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improving-earthquake-rapid-response-and-early-warning-performance-with-geodesy-and-machine-learning/125216/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the dissertation’s main goal regarding earthquake characterization?","Question",{"text":75,"@type":76},"To determine how quickly and accurately earthquakes can be characterized while and immediately after they occur, and to identify ways to improve the systems used for earthquake disaster response.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What do borehole strainmeter results suggest about early earthquakes by magnitude?",{"text":80,"@type":76},"Early earthquakes are not strongly deterministic, meaning large and small earthquakes do not appear inherently different at the start of their rupture processes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the machine-learning models improve early warning and magnitude estimation?",{"text":84,"@type":76},"One algorithm discriminates between GNSS waveforms that contain seismic waves and those dominated by noise, reducing low-quality inputs for magnitude determination. 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