An Algorithm for Shape-Based Distance of Microseismic Time Series Waveforms and its Application in Clustering Mining Events

An Algorithm for Shape-Based Distance of Microseismic Time Series Waveforms and its Application in Clustering Mining Events | 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 An Algorithm for Shape-Based Distance of Microseismic Time Series Waveforms and its Application in Clustering Mining Events Hao Luo, Ziyu Liu, Song Ge, Linlin Ding, Li Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5877330/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 To improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolutions Autoencoder (MDCAE) combined with a Constraints Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working face of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraints Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from \((O(n^2))\) to \((O(n \log n))\) , achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications. Microseismic event time series Multi-scale fusion convolution Feature extraction Volatility Shape similarity algorithm Unsupervised clustering 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-5877330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405582992,"identity":"bc106f4d-f43e-491f-9afd-246ef8366b60","order_by":0,"name":"Hao Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACPgYGNiBlU9/GzHzgwIcfRGhhg2hJY+xjZ0s8OLOHeC2HGefx8xgf5mAjRgt7j9mDHxVpzGzMPB8OM/AwyPOLHSCgheeMuWHPGRs2NmbeDYcLLBgMZ85OIKBFIsdMgrctjQesZQYPQ4LBbSK0SP5tOywBdNiDwzxsRGqR5m07bADUwkCkFp5jZdIyZ9IS2JjZDICBLEHYL/zszdsk31TYJMj3H3784cMPG3l+aQJa0IEEacpHwSgYBaNgFGAHAAKLOj0lxNebAAAAAElFTkSuQmCC","orcid":"","institution":"Liaoning University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Luo","suffix":""},{"id":405582993,"identity":"791373cd-6927-40d8-bf04-46708f07bfec","order_by":1,"name":"Ziyu Liu","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Liu","suffix":""},{"id":405582994,"identity":"cac2f9e9-b013-41fc-847e-06a9e53c2a8d","order_by":2,"name":"Song Ge","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Ge","suffix":""},{"id":405582995,"identity":"fa6acc31-57c9-4af7-86a1-43a7837e0a1a","order_by":3,"name":"Linlin Ding","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Ding","suffix":""},{"id":405582996,"identity":"cf62b0c8-6353-4239-bd59-aa80e5838e49","order_by":4,"name":"Li Zhang","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-22 04:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5877330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5877330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74693906,"identity":"f2fedc90-e117-4656-a28d-2cb3a9146744","added_by":"auto","created_at":"2025-01-24 19:31:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1333473,"visible":true,"origin":"","legend":"","description":"","filename":"AnAlgorithmforShapeBasedDistanceofMicroseismicTimeSeriesWaveformsanditsApplicationinClusteringMiningEvents.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5877330/v1_covered_a646bdc6-9e82-4f34-a1b0-41a99fb03672.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Algorithm for Shape-Based Distance of Microseismic Time Series Waveforms and its Application in Clustering Mining Events","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":"Microseismic event time series, Multi-scale fusion convolution, Feature extraction, Volatility, Shape similarity algorithm, Unsupervised clustering","lastPublishedDoi":"10.21203/rs.3.rs-5877330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5877330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolutions Autoencoder (MDCAE) combined with a Constraints Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working face of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraints Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((O(n^2))\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((O(n \\log n))\\)\u003c/span\u003e\u003c/span\u003e, achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications.\u003c/p\u003e","manuscriptTitle":"An Algorithm for Shape-Based Distance of Microseismic Time Series Waveforms and its Application in Clustering Mining Events","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 19:07:24","doi":"10.21203/rs.3.rs-5877330/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":"f03d538a-de05-4d2a-ab70-be26ff4f1565","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T19:15:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 19:07:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5877330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5877330","identity":"rs-5877330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}