Moment Magnitude Estimation Using Machine Learning Algorithms with an Application to the Western United States

Moment Magnitude Estimation Using Machine Learning Algorithms with an Application to the Western United States | 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 Moment Magnitude Estimation Using Machine Learning Algorithms with an Application to the Western United States Najme Alidadi, Shahram Pezeshk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303864/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 The moment magnitude (M W ) is crucial for risk assessment, seismic hazard analysis, and other seismological and geotechnical studies. In this study, we use various machine learning (ML) algorithms to estimate M W of earthquakes using peak ground acceleration (PGA) 5%-damped pseudo-spectral acceleration (PSA) at 21 different periods, hypocentral distance (R hypo ), the depth to the top of the rupture area (Z tor ), fault mechanism (FM), and timed-average shear-wave velocity of the upper 30 m of soil (V S30 ) as input parameters. In this study, we train and test different ML algorithms, including Support Vector Regression, Kernel Ridge Regression, Random Forest Regression, Gradient Boosting, and Neural Networks. Then, we use an ensemble model to combine the models mentioned. We utilize the NGA-West2 database. We focus on data characterized by M W for both training and testing purposes. Then, we estimate the M W for records where the magnitude type is not specified as M W . Results indicate that by using ML, we can quickly and accurately estimate M W for both new seismic events and the updated existing NGA-West2 flatfile for events that M W is inferred from other magnitude metrics. Additionally, this method can be used to address discrepancies in M W values across different earthquake catalogs. Physical sciences/Engineering Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences moment magnitude estimation machine learning seismic hazard analysis ground motion model Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryCode.zip 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. 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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-7303864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502934083,"identity":"ed632b1f-ddeb-4308-9f95-cf8785f6f04f","order_by":0,"name":"Najme Alidadi","email":"","orcid":"","institution":"The University of Memphis","correspondingAuthor":false,"prefix":"","firstName":"Najme","middleName":"","lastName":"Alidadi","suffix":""},{"id":502934087,"identity":"6c709b7b-74f1-49d7-8aab-aae6ac2c5763","order_by":1,"name":"Shahram Pezeshk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPCCAwb8IIqxQQJEGRCnRbKBZC0GB8BaGAhrkZ/dfPjDhz93jI2vHT66uXCHhT0De/M2CXxaDO4cSzCc2fbMzOx2WtrtmWckEht4jpXh1yKRY5DM23DYxux2jtlt3jaJBAaJHDO8WuRn5H84/OfPYRvj2fnfQFrsGeTf4NfCcCOHsZmB7bCZgXQOG0gLMNB48GsB+sWYsbftsLHE7TSz2zPbJBLbeNKKLfA6bHbz4w8//hw27J+d/Ox2YVudPT/74Y038DoM2Q3MIIINr3KsWkbBKBgFo2AUoAMA0WZNj5N9khkAAAAASUVORK5CYII=","orcid":"","institution":"The University of memphis","correspondingAuthor":true,"prefix":"","firstName":"Shahram","middleName":"","lastName":"Pezeshk","suffix":""}],"badges":[],"createdAt":"2025-08-05 20:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7303864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7303864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97136982,"identity":"ba05f19e-822d-4f1b-8bf6-7730cbfe5491","added_by":"auto","created_at":"2025-12-01 09:57:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065368,"visible":true,"origin":"","legend":"","description":"","filename":"AlidadiandPezeshkMwAugust92025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303864/v1_covered_1940647d-9a7b-4da4-9b03-843272a9d1bd.pdf"},{"id":89909137,"identity":"d1c6411a-2083-41b3-9b5c-ba12be59e233","added_by":"auto","created_at":"2025-08-26 10:36:13","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18053,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryCode.zip","url":"https://assets-eu.researchsquare.com/files/rs-7303864/v1/ff529e8b79b2d498291bea56.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Moment Magnitude Estimation Using Machine Learning Algorithms with an Application to the Western United States","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":"moment magnitude estimation, machine learning, seismic hazard analysis, ground motion model","lastPublishedDoi":"10.21203/rs.3.rs-7303864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe moment magnitude (M\u003csub\u003eW\u003c/sub\u003e) is crucial for risk assessment, seismic hazard analysis, and other seismological and geotechnical studies. In this study, we use various machine learning (ML) algorithms to estimate M\u003csub\u003eW\u003c/sub\u003e of earthquakes using peak ground acceleration (PGA) 5%-damped pseudo-spectral acceleration (PSA) at 21 different periods, hypocentral distance (R\u003csub\u003ehypo\u003c/sub\u003e), the depth to the top of the rupture area (Z\u003csub\u003etor\u003c/sub\u003e), fault mechanism (FM), and timed-average shear-wave velocity of the upper 30 m of soil (V\u003csub\u003eS30\u003c/sub\u003e) as input parameters. In this study, we train and test different ML algorithms, including Support Vector Regression, Kernel Ridge Regression, Random Forest Regression, Gradient Boosting, and Neural Networks. Then, we use an ensemble model to combine the models mentioned. We utilize the NGA-West2 database. We focus on data characterized by M\u003csub\u003eW\u003c/sub\u003e for both training and testing purposes. Then, we estimate the M\u003csub\u003eW\u003c/sub\u003e for records where the magnitude type is not specified as M\u003csub\u003eW\u003c/sub\u003e. Results indicate that by using ML, we can quickly and accurately estimate M\u003csub\u003eW\u003c/sub\u003e for both new seismic events and the updated existing NGA-West2 flatfile for events that M\u003csub\u003eW\u003c/sub\u003e is inferred from other magnitude metrics. 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