Microseismic source location based on full waveform inversion driven neural network

Microseismic source location based on full waveform inversion driven neural network | 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 Research Article Microseismic source location based on full waveform inversion driven neural network Yan Zhang, Zixin Wei, Yonngxue Zhang, Hongli Dong, Jingzhe Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6459246/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Accurate localization of microseismic sources is essential in fields such as oil and gas extraction and underground energy storage. Current seismic source localization methods based on full waveform inversion exhibit a high degree of nonlinearity and involve complex gradient calculations for the objective function. However, data-driven neural network microseismic source localization methods lack physical constraints, which can compromise geological validity. To address these challenges, this paper proposes a microseismic source localization method that integrates full waveform inversion with a recurrent neural network. First, the seismic wavefield propagation operator is designed using convolutional kernels to achieve networked microseismic forward modeling. Next, chain differentiation of the neural network is employed to calculate the gradient for full waveform inversion in reverse, improving computational efficiency. Finally, by minimizing the error between the observed and forward-modeled data, the spatial components of the seismic source are optimized, and non-maximum suppression is applied to obtain the spatial location of the seismic source. The experimental results reveal that the proposed method achieves high localization accuracy, high computational efficiency, and resistance to noise. Full Waveform Inversion Gradient Calculation Microseismic Source Location Recurrent Neural Networks Source Spatial Component Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers invited by journal 25 Apr, 2025 Editor assigned by journal 24 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 15 Apr, 2025 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-6459246","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448929530,"identity":"db5aaa8c-847b-408a-8c04-f2c25f8c66de","order_by":0,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCSjNxt588MEHNEH8Wvh4jiUbziBJi5xEjpk0DzFa5Gc3P3v4tc0ujw2kxXaHTZ7BAeaDt3kY7PJwaWGcc8zcWLYtuZiN51mxde6ZtGKDA2zJ1jwMycW4tDBLJJhJS7YxJ7axJ2+8ndt2OHHDAR6QCw8kNuDQwiaR/g2opT6xjSHBQNqy7T9QC/83vFp4gF6Q/Ag0vI0jxUiase0AyBY2vFokJHLKpBnOHU9sAwVyL9BTkofZjC3nGCTj1CI/I32b5I+y6sT57cCo/AkMOr7jzQ9vvKmww6kFHAS8bAhOAgMziDLAox4IGH/8QdYyCkbBKBgFowANAACU41Xaaj3+owAAAABJRU5ErkJggg==","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":448929531,"identity":"90747d96-de83-4917-9eca-4dd9d4348d73","order_by":1,"name":"Zixin Wei","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Wei","suffix":""},{"id":448929532,"identity":"a66fe07c-87dd-4e3c-a7c6-89e1927a5e07","order_by":2,"name":"Yonngxue Zhang","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Yonngxue","middleName":"","lastName":"Zhang","suffix":""},{"id":448929534,"identity":"1a6d41d4-79c0-47f8-b4f1-c06f95620a7c","order_by":3,"name":"Hongli Dong","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Hongli","middleName":"","lastName":"Dong","suffix":""},{"id":448929535,"identity":"ece02729-425b-4a17-9f37-b16e7f601a2e","order_by":4,"name":"Jingzhe Wang","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhe","middleName":"","lastName":"Wang","suffix":""},{"id":448929536,"identity":"114a7b58-19b5-46fc-80ec-8bde91cd739b","order_by":5,"name":"Linjun Zhang","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Linjun","middleName":"","lastName":"Zhang","suffix":""},{"id":448929537,"identity":"c4c2c733-b549-48e6-9954-576422f8f657","order_by":6,"name":"Liwei Song","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Liwei","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-04-16 03:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6459246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6459246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82144925,"identity":"aa3fd1aa-ff71-46fe-ae7b-b32b850f69ab","added_by":"auto","created_at":"2025-05-07 06:49:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":887143,"visible":true,"origin":"","legend":"","description":"","filename":"M5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6459246/v1_covered_fff69c0b-ee26-440c-a63d-5e728dd2b050.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microseismic source location based on full waveform inversion driven neural network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Full Waveform Inversion, Gradient Calculation, Microseismic Source Location, Recurrent Neural Networks, Source Spatial Component","lastPublishedDoi":"10.21203/rs.3.rs-6459246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6459246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate localization of microseismic sources is essential in fields such as oil and gas extraction and underground energy storage. Current seismic source localization methods based on full waveform inversion exhibit a high degree of nonlinearity and involve complex gradient calculations for the objective function. However, data-driven neural network microseismic source localization methods lack physical constraints, which can compromise geological validity. To address these challenges, this paper proposes a microseismic source localization method that integrates full waveform inversion with a recurrent neural network. First, the seismic wavefield propagation operator is designed using convolutional kernels to achieve networked microseismic forward modeling. Next, chain differentiation of the neural network is employed to calculate the gradient for full waveform inversion in reverse, improving computational efficiency. Finally, by minimizing the error between the observed and forward-modeled data, the spatial components of the seismic source are optimized, and non-maximum suppression is applied to obtain the spatial location of the seismic source. The experimental results reveal that the proposed method achieves high localization accuracy, high computational efficiency, and resistance to noise.\u003c/p\u003e","manuscriptTitle":"Microseismic source location based on full waveform inversion driven neural network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:17:09","doi":"10.21203/rs.3.rs-6459246/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-05T13:12:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T16:19:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T06:28:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187691336674488771057063305666639417106","date":"2025-06-02T01:25:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98329484154781014078705869663223752902","date":"2025-06-01T16:33:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257989909223742855827631855613531050240","date":"2025-04-25T12:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T11:36:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T03:09:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-23T08:22:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Computational Intelligence Systems","date":"2025-04-16T03:48:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a06372ce-c611-4fa9-8f91-8fc79a9ab371","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T21:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:17:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6459246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6459246","identity":"rs-6459246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}