Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence 

Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence Kameron Bustos, Abbas Maazallahi, Mohammad Amir Salari, Eli Snir, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4249733/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Seismic events present a significant global threat, underscoring the need for effective models to provide insights into these natural disasters. This paper addresses the critical need for advanced seismic event analysis by combining traditional data analysis with cutting-edge machine learning models. The primary objective is to develop models that classify seismic events into different types based on their geological and seismic characteristics and forecast their magnitude. The seismic activities categorized into groups by magnitude to enhance the understanding of these phenomena. Location-Based and Seismic Characteristics Features are utilized in seven machine learning models: Rule-Based Classifier, K-mean Classifier, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Logistic Regression. This approach aims to provide valuable insights into seismic activities, contributing to the development of more nuanced disaster analysis and early warning systems. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4249733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294634706,"identity":"beaf4eed-aede-47cf-ba7f-ac1303eefc00","order_by":0,"name":"Kameron Bustos","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Kameron","middleName":"","lastName":"Bustos","suffix":""},{"id":294634708,"identity":"c2e416db-5d1e-46c8-92b9-3fe39aeeabe6","order_by":1,"name":"Abbas Maazallahi","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Maazallahi","suffix":""},{"id":294634710,"identity":"794cb874-ec2e-4973-a28e-0dd88f3d9ef7","order_by":2,"name":"Mohammad Amir Salari","email":"","orcid":"","institution":"Washington University in Saint Louis","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amir","lastName":"Salari","suffix":""},{"id":294634712,"identity":"7f0f177f-f19a-4f64-a4c4-5096ad5d74e8","order_by":3,"name":"Eli Snir","email":"","orcid":"","institution":"Washington University in Saint Louis","correspondingAuthor":false,"prefix":"","firstName":"Eli","middleName":"","lastName":"Snir","suffix":""},{"id":294634713,"identity":"2c86bb32-86eb-4e6c-8aa1-587bfe100d7d","order_by":4,"name":"Payam Norouzzadeh","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Payam","middleName":"","lastName":"Norouzzadeh","suffix":""},{"id":294634714,"identity":"88fb990c-1053-413b-9203-2628b6b1757e","order_by":5,"name":"Bahareh Rahmani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACAwYeEHUAiMEMCTkGdhDNRoIWYwZmErUwJDYQ0mLOfvbg54qKOwzm7WePbvi5wyJ9fjOPAcOHssM4tVj25CVLnjnzjEHmTF7azd4zErkbDvMYMM44h1uLwYEcA8nGtsMMEgw5Zjd424BamHkMmHnb8Gg5/8b4Z+M/oBb+N2Y3/7ZJpMsDHcb8F5+WGzlmko0NQC0SOWa3gbYkMAAdxsyIR4vljDdmlg3HDvNISLwxuy3bJmG44TBbwcGec+k4tZjz5xjfbKg5LCfBn2N2821bnbx8e/PGBz/KrHFqgQEeFN4BgupHwSgYBaNgFOAFAOhuVEYqfJXoAAAAAElFTkSuQmCC","orcid":"","institution":"Saint Louis University","correspondingAuthor":true,"prefix":"","firstName":"Bahareh","middleName":"","lastName":"Rahmani","suffix":""}],"badges":[],"createdAt":"2024-04-11 01:59:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4249733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4249733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56427436,"identity":"06ebd3d0-5359-4968-9181-daf6626aac36","added_by":"auto","created_at":"2024-05-14 04:54:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588265,"visible":true,"origin":"","legend":"","description":"","filename":"Final1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4249733/v1_covered_92a2c9ea-76f0-4776-b29e-f3d7f4b68e64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4249733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4249733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Seismic events present a significant global threat, underscoring the need for effective models to provide insights into these natural disasters. This paper addresses the critical need for advanced seismic event analysis by combining traditional data analysis with cutting-edge machine learning models. The primary objective is to develop models that classify seismic events into different types based on their geological and seismic characteristics and forecast their magnitude. The seismic activities categorized into groups by magnitude to enhance the understanding of these phenomena. Location-Based and Seismic Characteristics Features are utilized in seven machine learning models: Rule-Based Classifier, K-mean Classifier, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Logistic Regression. This approach aims to provide valuable insights into seismic activities, contributing to the development of more nuanced disaster analysis and early warning systems.","manuscriptTitle":"Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 07:03:37","doi":"10.21203/rs.3.rs-4249733/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9871e8fd-82df-4658-a925-c5c9f01d6f81","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31061151,"name":"Earth and environmental sciences/Environmental sciences"},{"id":31061152,"name":"Earth and environmental sciences/Natural hazards"},{"id":31061153,"name":"Physical sciences/Engineering"},{"id":31061154,"name":"Physical sciences/Mathematics and computing"},{"id":31061155,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2024-05-14T04:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 07:03:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4249733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4249733","identity":"rs-4249733","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}