[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117290-en":3,"doc-seo-117290-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117290,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","SHARP Storage - Deliverable 4.3 - Machine learning approaches for earthquake detection","The report reviews how artificial intelligence, machine learning, and deep learning techniques are applied to earthquake detection using seismic data. It first covers traditional earthquake detection concepts and the main challenges of conventional workflows. It then defines AI/ML/DL, summarizes supervised, unsupervised, and reinforcement learning, and compares deep learning with traditional machine learning. The review synthesizes deep-learning architectures and current detection algorithms, and discusses how ML/DL can support SHARP activities for seismic and CO2 storage monitoring, including key limitations and future directions.","SHARP Storage – Project no 327342  \nDeliverable 4.3  \nMachine learning approaches for earthquake detection  \n| Organisation(s) | University of Oxford, Equinor, NORSAR |\n| --- | --- |\n| Author(s) | Tom Kettlety\u003Cbr>Tesfahiwet Abraha\u003Cbr>J Michael Kendall |\n| Reviewer | Anne-Kari Furre\u003Cbr>Nadege Langet |\n| Type of deliverable | Report |\n| Dissemination level | Open |\n| WP | 4 |\n| Issue date | 20 February 2024 |\n| Document version | 1 |\n\nKeywords:  \nSeismology, earthquake detection, machine learning, deep learning, phase picking.  \nSummary:  \nThis report is a review of machine learning-based methods for earthquake detection. It first introduces the basic concepts of traditional earthquake detection, and commonly used approaches. It then goes onto describe the concept of artificial intelligence, machine learning, and deep learning. This is followed with a summary of how these concepts are employed to detect earthquakes in seismic data, with a synthesis of the most widely used machine learning detection algorithms. The ways in which machine learning methods could be applied to the activities in the SHARP project are given in the conclusions, along with the future directions of the field.  \nSHARP storage project Deliverable 4.3: Machine learning approaches for microseismic detection  \nFebruary 2024  \nContents  \nExecutive summary 3  \n1 Introduction 4  \n1.1 Earthquake detection ...................................... 4  \n1.2 Challenges of conventional earthquake detection ...................... 7  \n2 Artificial intelligence, machine learning, and deep learning 9  \n2.1 Artificial Intelligence (AI) ..................................... 9  \n2.2 Machine learning: a subset of AI ................................ 11  \n2.3 Definitions and types of ML algorithms ............................ 13  \n2.3.1 Supervised learning ................................... 13  \n2.3.2 Unsupervised learning ................................. 14  \n2.3.3 Reinforcement learning ................................. 15  \n2.4 Machine learning applications ................................. 16  \n2.5 Deep learning: a sub-field of ML ................................ 16  \n2.5.1 Deep learning: definitions and significance ...................... 17  \n2.5.2 Deep learning vs.“traditional” ML ........................... 17  \n2.6 Deep learning architectures for earthquake detection ................... 18  \n2.6.1 Multi-layer perceptron ................................. 18  \n2.6.2 Convolutional neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  \n2.6.3 Recurrent neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20  \n2.6.4 Generative networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  \n2.6.5 Hybrid and other architectures ............................. 22  \n2.6.6 Deep transfer learning ................................. 23  \n2.6.7 Deep reinforcement learning .............................. 23  \n2.6.8 Transformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23  \n3 Machine learning in seismology 24  \n3.1 Data and preprocessing ..................................... 24  \n3.2 ML and DL applications in geophysics ............................. 25  \n3.2.1 Phase picking and earthquake detection ....................... 25  \n3.3 Deep learning architectures for detection ........................... 25  \n3.3.1 Transfer Learning .................................... 26  \n3.4 Current ML detection algorithms ................................ 26  \n3.5 Machine learning toolbox for seismology ........................... 28  \n4 Machine learning in CO2 storage monitoring 29  \n4. 1 SHARP activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29  \n5 Challenges and future directions 31  \n5.1 Challenges in Machine Learning-Based Earthquake Detection ............... 31  \n5.2 Future Directions and Emerging Technologies ........................ 31  \nExecutive summary  \nThis ","cbCaidHfRI1EA8Qk","https://ap.wps.com/l/cbCaidHfRI1EA8Qk","pdf",4075567,1,44,"English","en",105,"# Executive summary\n# Introduction\n## Earthquake detection\n## Challenges of conventional earthquake detection\n# Artificial intelligence, machine learning, and deep learning\n## Artificial Intelligence (AI)\n## Machine learning: a subset of AI\n## Definitions and types of ML algorithms\n## Machine learning applications\n## Deep learning: a sub-field of ML\n## Deep learning architectures for earthquake detection\n# Machine learning in seismology\n## Data and preprocessing\n## ML and DL applications in geophysics\n## Deep learning architectures for detection\n## Current ML detection algorithms\n## Machine learning toolbox for seismology\n# Machine learning in CO2 storage monitoring\n## SHARP activities\n# Challenges and future directions\n## Challenges in Machine Learning-Based Earthquake Detection\n## Future Directions and Emerging 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does the report review about earthquake detection methods?","Question",{"text":74,"@type":75},"It reviews machine learning-based approaches for earthquake detection, starting from traditional detection concepts and then detailing AI, ML, and deep learning methods used on seismic data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are ML and DL concepts structured in the report?",{"text":79,"@type":75},"The report introduces AI, defines machine learning and deep learning, distinguishes learning types such as supervised, unsupervised, and reinforcement learning, and summarizes deep learning architectures relevant to detection.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the report connect ML approaches to SHARP and CO2 storage monitoring?",{"text":83,"@type":75},"It explains how ML/DL methods could be applied to SHARP activities beyond seismic detection, including geological CO2 storage monitoring, and concludes with challenges and future research 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