[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123299-en":3,"doc-seo-123299-105":30,"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":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},123299,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Application of Artificial Intelligence and Machine Learning in Seismological Studies","Seismological studies have traditionally relied on classical statistical models and manual interpretation to detect, analyze, and predict earthquake events. Growing seismic data complexity and volume make more efficient, adaptive methods necessary. The study reviews how artificial intelligence and machine learning—especially deep learning—support seismic data processing and interpretation, focusing on CNNs, RNNs, GANs, SVMs, clustering, and AI systems that improve detection, prediction, and earthquake analysis while strengthening overall analytical accuracy.","Application of Artificial Intelligence and Machine Learning in Seismological Studies  \nTolulope Esther Awopejoa *, Peter Oluwasayo Adigunb, Nelson Abimbola  \nAyuba Azeezc  \naDepartment of Natural Resources Management (Geology), New Mexico Highlands University, 1005 Diamond  \nSt, Las Vegas, New Mexico, USA  \nbDepartment of Computer Science, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New  \nMexico, USA  \ncDepartment of Physics, University of Abuja, Abuja, Federal Capital Territory, Nigeria  \naEmail: [tawopejo@live.nmhu.edu](tawopejo@live.nmhu.edu)  \n[b](bEmail: poadigun@nmhu.edu)[Email: poadigun@nmhu.edu](bEmail: poadigun@nmhu.edu)  \ncEmail: [azeez.abimbola2019@uniabuja.edu.ng](azeez.abimbola2019@uniabuja.edu.ng)  \nAbstract  \nSeismological studies have traditionally relied on classical statistical models and manual interpretation to detect, analyze, and predict earthquake events. However, the growing complexity and volume of seismic data have necessitated more efficient and adaptive approaches. This study explores the integration of artificial intelligence (AI) and machine learning (ML) techniques into seismology. This study highlighted the capacity of AI and ML to revolutionize seismic data processing and interpretation. Majorly, the study reviewed findings on algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods. Also, AI systems such as WaveCastNet, SCALODEEP, BNGCNN, Cycle-Jnet, SASMEX, and UREDAS were reviewed in areas that improved accuracy in earthquake detection, earthquake predictions, and earthquake analysis.  \nKeywords: Artificial Intelligence (AI); Machine Learning (ML); Deep Learning (DL); Convolutional Neural Network (CNN) .  \nReceived: 3/30/2025  \nAccepted: 5/12/2025  \nPublished: 5/23/2025  \n* Corresponding author.  \n1. Introduction  \nThe increasing frequency and devastating impact of earthquakes worldwide have prompted the urgent need for advancements in seismological monitoring, prediction, and hazard mitigation. Traditional methods in seismology, though reliable, often face limitations in processing the ever-growing volume of seismic data and capturing the complex, nonlinear patterns associated with earthquake phenomena. In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into seismological studies has emerged as a transformative approach, enhancing the precision, efficiency, and depth of seismic data analysis [1, 2] .  \nArtificial intelligence (AI) and machine learning (ML) have significantly advanced seismological studies, improving earthquake detection, prediction, and analysis. Artificial intelligence (AI) and machine learning (ML) are increasingly transforming the field of seismology by offering new ways to detect, analyze, and interpret seismic events [3] . Mostly, applications of AI and ML are closely related but have different concepts. Traditionally, seismological studies relied on manual analysis and statistical models to detect earthquakes and understand seismic wave behavior [4] . The contemporary issues in seismology problems like earthquake detection, phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy [4-6] . The applications ofAI and ML are illustrated in Figure 1.  \nFigure 1: Application of AI and ML in Seismology  \nHowever, the surge in seismic data from global sensor networks has created an opportunity for AI and ML to provide more efficient and accurate tools for seismic analysis [7] . These technologies are now being applied in various aspects of seismology, including real-time earthquake detection, classification of seismic events,  \nmagnitude estimation, and seismic signal denoising [6-10] . AI-based models help identify unseen seismic signals, extract features, and enhance early warning systems [3, 9, 11-13] . Moreover, studies","cbCaiiqjcdVCG89K","https://ap.wps.com/l/cbCaiiqjcdVCG89K","pdf",579666,1,19,"English","en",105,"# Abstract\n# Introduction\n# Applications of Artificial Intelligence and Machine Learning in Seismology","[{\"question\":\"Why are AI and machine learning needed in seismological studies?\",\"answer\":\"They address limitations of classical statistical models and manual interpretation by enabling more efficient handling of large, complex seismic datasets and capturing nonlinear patterns related to earthquakes.\"},{\"question\":\"Which AI and ML techniques are highlighted in the study?\",\"answer\":\"The review discusses algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods.\"},{\"question\":\"For what seismological tasks do AI-based models provide improvements?\",\"answer\":\"AI and ML models are used for tasks including earthquake detection, seismic phase picking, event localization, magnitude estimation, ground-motion prediction, signal denoising, and supporting early warning systems.\"}]","Application of Artificial Intelligence and Machine Learning in Seismological Studies | 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are AI and machine learning needed in seismological studies?","Question",{"text":76,"@type":77},"They address limitations of classical statistical models and manual interpretation by enabling more efficient handling of large, complex seismic datasets and capturing nonlinear patterns related to earthquakes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which AI and ML techniques are highlighted in the study?",{"text":81,"@type":77},"The review discusses algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), support vector machines (SVMs), and unsupervised clustering methods.",{"name":83,"@type":74,"acceptedAnswer":84},"For what seismological tasks do AI-based models provide improvements?",{"text":85,"@type":77},"AI and ML models are used for tasks including earthquake detection, seismic phase picking, event localization, magnitude estimation, ground-motion prediction, signal denoising, and supporting 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