[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128639-en":3,"doc-seo-128639-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128639,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Advances in Machine Learning for Seismic Event Detection","Advances in computational capabilities are enabling data-driven modelling that exploits statistical properties of large datasets. Machine learning automatically discovers governing relationships within data distributions, and seismology benefits from long-term, high-quality seismic recording catalogs used across many studies. Limited adoption of recent ML research motivates potential performance gains for traditional seismic tasks, especially detecting arriving seismicity and improving event catalogs. This thesis develops ML methods for phase arrival identification and scalable, robust seismic phase association, introduces SeisBench for evaluation and pipeline support, and assesses model performance across benchmark and practical seismic environments.","Advances in Machine Learning for Seismic Event  \nDetection  \nZur Erlangung des akademischen Grades eines DOKTORS DER NATURWISSENSCHAFTEN (Dr. rer. nat.)  \nvon der KIT-Fakultät für Physik des  \nKarlsruher InstitutesfürTechnologie (KIT)  \nangenommene  \nDISSERTATION  \nvon  \nM.Sc. Jack Woollam  \nTag der mündlichen Prüfung  \n11.11.2022  \nReferent: Prof. Dr. Andreas Rietbrock  \nKorreferent: Prof. Dr. Frederik Tilmann  \nKorreferent: Prof. Dr. Joachim Ritter  \nii  \nAbstract  \nAdvances in computational power and storage are facilitating a new era of modelling. As the amount of information captured within datasets has evolved, tools have emerged to better exploit the statistical properties of such datasets. Machine Learning (ML) methods are one such family of techniques, leading the revolution of ’data-driven’ solutions which achieve state-of-the-art performance in solving tasks across both business and the sciences. ML is concerned with the automated discovery of the governing relationships within data distributions. The algorithms can be thought of as a suite of generalized algorithms for extracting information. Seismology is a field naturally suited to the application of ML, containing high-quality catalogs of seismic recordings-collected over decades-which are crucial inputs into many seismological studies.  \nWith seismology only starting to widely integrate the latest state-of-the-art ML research over the last few years, the lack of uptake means that huge performance increases may be possible for traditional tasks. The detection of arriving seismicity is one such area. Having more complete seismic catalogs means imaging smaller magnitude events,’closing the gap’between seismicity observed in nature and what can be simulated in laboratory environments. Better-resolution seismic catalogs are, therefore, crucial for enhancing any physical understanding of seismogenic rupture processes.  \nThis thesis investigates the extent to which ML can improve the task of detecting seismic events. We first explore and propose new methods for seismic phase arrival identification, applying a Convolutional Neural Network (CNN) trained for supervised classification of labelled seismic arrivals. We also propose a novel algorithm for the subsequent stage of the event detection pipeline, the task of seismic phase association. Our proposed association algorithm is designed to operate efficiently in a scalable and robust manner. We adapt a parametric model fitting framework to extract a physical model of the seismic wavefield moveout to associate picks to events. We then turn to the question of how to best evaluate the state-of-the-art ML in seismology. Here, we can also leverage practices from ML-focused fields such as computer vision, and natural language processing. Access to both open-access benchmark datasets and models is crucial for accelerating the research process. This thesis introduces software specifically designed for this task-SeisBench. It aims to significantly reduce the amount of work required to conduct ML research in seismology, accelerating development and iteration. We finally explore how the performance of the latest ML models varies when moving from well-curated benchmark training datasets to practical pipelinesin less well-explored seismic environments.  \nWe hope our exploratory work and subsequent results, including productionized software, will facilitate further in-depth research in applying ML to one of the most fundamental tasks in seismology.  \nAcknowledgements  \nI would like to express my complete thanks to my supervisor Prof. Andreas Rietbrock, who throughout both my PhD studies, all the way through to my undergraduate years at the University of Liverpool in the UK, has provided invaluable mentorship, guidance, and an infectious attitude towards tackling difficult problems which I will take with me throughout the rest of my life. He has been the primary driver behind my personal development over the years from my initial mino","cbCaiscPbL0JfB7G","https://ap.wps.com/l/cbCaiscPbL0JfB7G","pdf",31035636,2,1,131,"English","en",105,"# Introduction\n## Motivation\n## The seismic event detection task\n## Aim and scope of the thesis\n## Structure\n## Contributions\n# Theory\n## Machine learning overview\n## Supervised machine learning\n### Classification\n### Regression\n## Supervised Deep Learning: Neural Networks\n### The Perceptron\n### Neural Network Design\n### How to train a neural network\n### Choice of activation function\n### Neural Network Variants\n## Methods of seismic event detection","[{\"question\":\"What problem does this thesis address in seismology?\",\"answer\":\"It investigates how machine learning can improve the detection of seismic events, focusing on arriving seismicity and building more complete seismic catalogs.\"},{\"question\":\"How are seismic phases identified in the proposed approach?\",\"answer\":\"The thesis explores and proposes methods for seismic phase arrival identification using a convolutional neural network trained for supervised classification of labeled seismic arrivals.\"},{\"question\":\"What is the purpose of the proposed seismic phase association algorithm?\",\"answer\":\"It supports the event detection pipeline by associating seismic picks to events efficiently, designed to be scalable and robust through a parametric model fitting framework.\"}]","Advances in Machine Learning for Seismic Event Detection | 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problem does this thesis address in seismology?","Question",{"text":76,"@type":77},"It investigates how machine learning can improve the detection of seismic events, focusing on arriving seismicity and building more complete seismic catalogs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are seismic phases identified in the proposed approach?",{"text":81,"@type":77},"The thesis explores and proposes methods for seismic phase arrival identification using a convolutional neural network trained for supervised classification of labeled seismic arrivals.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the purpose of the proposed seismic phase association algorithm?",{"text":85,"@type":77},"It supports the event detection pipeline by associating seismic picks to events efficiently, designed to be scalable and robust through a parametric model fitting 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