[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121832-en":3,"doc-seo-121832-105":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":4,"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},121832,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Unsupervised Machine-Learning Applications in Seismology - Thesis Summary","Seismic catalogs of hypocenter locations, origin times, and magnitudes underpin research into Earth structure, seismic-event character, and fault-system dynamics. While supervised machine learning has improved catalog building, capturing time-varying spectral structure of waveform content remains limited. This thesis develops SpecUFEx—spectral unsupervised feature extraction—using non-negative matrix factorization and hidden Markov modeling, scaled via stochastic variational inference. The SpecUFEx+ workflow enables pre-processing, clustering, and visualization for large spectrogram sets, applied to icequakes and tremors at Gornergletscher and repeating earthquakes on the San Andreas Fault.","Unsupervised Machine-Learning Applications in Seismology  \nTheresa Sawi  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee  \nof the Graduate School of Arts and Sciences  \nCOLUMBIA UNIVERSITY  \n© 2023 Theresa Sawi  \nAll Rights Reserved  \nAbstract  \nCatalogs of seismic source parameters (hypocenter locations, origin times, and magnitudes) are vital for studying various Earth processes, greatly enhancing our understanding of the nature of seismic events, the structure of the Earth, and the dynamics of fault systems. Modern seismic analyses utilize supervised machine learning (ML) to build enhanced catalogs based on millions of examples of analyst-picked phase-arrivals in waveforms, yet the ability to characterize the time-varying spectral content of the waveforms underlying those catalogs remains lacking. Unsupervised machine learning (UML) methods provide powerful tools for inferring patterns from musical spectrograms with little a priori information, yet has been relatively underutilized in the field of seismology. In this thesis, I leverage advanced tools from UML to analyze the temporal spectral content of large sets of spectrograms generated by different mechanisms in two distinct geologic settings: icequakes and tremors at Gornergletscher (a Swiss temperate glacier) and repeating earthquakes from a 10-km-long creeping segment of the San Andreas Fault. The core algorithm in this work, now known as Spectral Unsupervised Feature Extraction, or SpecUFEx, extracts time-varying frequency patterns from spectrograms and reduces them into low-dimensionality fingerprints via a combination of non-negative matrix factorization and hidden Markov Modeling (Holtzman et al. 2018), optimized for large data sets via stochastic variational inference. This work describes the SpecUFEx algorithm and the suite of  \npre-processing, clustering, and visualization tools developed to create an UML workflow, SpecUFEx+, that is widely-accessible and applicable for many seismic settings. I apply the  \nSpecUFEx+ workflow to single-and multi-station seismic data from Gornergletscher, and demonstrate how some fingerprint-clusters track diurnal tremor related to subglacial water flow, while others correspond to the onset of the subglacial and englacial components of a glacial lake outburst flood. I also discover periods of harmonic tremor localized near the ice-bed interface that may be related to glacial stick-slip sliding. I additionally apply the SpecUFEx+ workflow to earthquakes on the San Andreas Fault to unveil far more repeating earthquake sequences than previously inferred, leading to enhanced slip-rate estimates at seismogenic depths and providing amore detailed image of seismic gaps along the fault interface. Unsupervised feature extraction is a novel tool to the field of seismology. This work demonstrates how scientific insight can be gained through the characterization of the spectral-temporal patterns of large seismic datasets within an UML-framework.  \nTable of Contents  \nAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv  \nDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv  \nIntroduction or Preface ..................................... 1  \nChapter 1: SpecUFEx+: A Python-based workflow for spectral unsupervised machine learning in seismology ................................. 9  \n1.1 Introduction ...................................... 9  \n1.2 The SpecUFEx algorithm ............................... 11  \n1.2.1 Spectrogram generation ............................ 12  \n1.2.2 Non-negative matrix factorization ...................... 12  \n1.2.3 Hidden Markov modeling ........................... 13  \n1.2.4 SpecUFEx parameter selection ........................ 14  \n1.3 Clustering and evaluation ............................... 15  \n1.3.1 Principal component analysis .....................","cbCaib53Cl5NYH4R","https://ap.wps.com/l/cbCaib53Cl5NYH4R","pdf",28491518,1,138,"English","en",105,"# Introduction or Preface\n# Chapter 1: SpecUFEx+: A Python-based workflow for spectral unsupervised machine learning in seismology\n# Chapter 2: An Unsupervised Machine-Learning-Approach to Understanding Seismicity atan Alpine Glacier","[{\"question\":\"What gap in seismic analysis motivates this thesis?\",\"answer\":\"The thesis targets the limitation of supervised ML catalogs to characterize the time-varying spectral content of waveforms underlying those catalogs.\"},{\"question\":\"How does SpecUFEx extract features from spectrograms?\",\"answer\":\"SpecUFEx extracts time-varying frequency patterns, then reduces them into low-dimensional fingerprints using non-negative matrix factorization combined with hidden Markov modeling.\"},{\"question\":\"What scientific insights does SpecUFEx+ provide in the case studies?\",\"answer\":\"In Gornergletscher data, fingerprint clusters track diurnal tremor and phases related to glacial lake outburst floods, while harmonic tremor periods near the ice-bed interface suggest stick-slip behavior. In the San Andreas Fault case, the workflow reveals more repeating earthquake sequences and refines slip-rate estimates and seismic-gap structure.\"}]","Unsupervised Machine-Learning Applications in Seismology - Thesis Summary | PDF",1785807126,348,{"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},"unsupervised-machine-learning-applications-in-seismology-thesis-summary","",{"@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/unsupervised-machine-learning-applications-in-seismology-thesis-summary/121832/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What gap in seismic analysis motivates this thesis?","Question",{"text":75,"@type":76},"The thesis targets the limitation of supervised ML catalogs to characterize the time-varying spectral content of waveforms underlying those catalogs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SpecUFEx extract features from spectrograms?",{"text":80,"@type":76},"SpecUFEx extracts time-varying frequency patterns, then reduces them into low-dimensional fingerprints using non-negative matrix factorization combined with hidden Markov modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"What scientific insights does SpecUFEx+ provide in the case studies?",{"text":84,"@type":76},"In Gornergletscher data, fingerprint clusters track diurnal tremor and phases related to glacial lake outburst floods, while harmonic tremor periods near the ice-bed interface suggest stick-slip behavior. In the San Andreas Fault case, the workflow reveals more repeating earthquake sequences and refines slip-rate estimates and seismic-gap structure.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]