Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model

Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model | 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 Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model Fan Yang, Menghui Zhi, Yan An This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4474949/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Jincheng City's mining areas have long been plagued by surface subsidence, posing significant threats to local residents' safety and impacting the region's economic and social stability. Understanding and effectively monitoring the driving factors and mechanisms of surface subsidence are crucial for devising scientific prevention measures and promoting the sustainable development of mining areas. This article aims to comprehensively reveal the large-scale surface subsidence phenomenon in Jincheng City's mining clusters by utilizing advanced remote sensing technology and machine learning models, identifying its main driving forces, and predicting future subsidence trends to provide scientific evidence for geological disaster prevention in mining areas. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, using both Permanent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset Interferometric SAR (SBAS-InSAR) techniques for cross-validation, to confirm the existence of surface subsidence. Further, by integrating Variational Mode Decomposition (VMD), Singular Spectrum Analysis (SSA), and Long Short-Term Memory (LSTM) networks, a high-precision time series prediction model (VMD-SSA-LSTM) was developed. The results indicate that from 2018 to 2021, the surface subsidence rates in Jincheng City ranged from − 34 to 34 millimeters per year, with significant variations in subsidence levels across different areas. Gaoping City exhibited the highest subsidence, with rates ranging from − 34 to 5 mm per year, while Yangcheng County showed the most pronounced subsidence changes. These variations are primarily attributed to mining activities, land use changes, and adverse geological conditions in Jincheng City. This study unveils the large-scale surface subsidence phenomenon in Jincheng City's mining clusters, marking the first comprehensive ground deformation monitoring analysis of small mining clusters across four cities in Jincheng. The development of a high-precision surface subsidence prediction model provides new insights for scientifically understanding geological disasters in mining areas. These findings are significant for formulating effective geological disaster prevention measures and land management policies. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Surface Subsidence MT-InSAR VMD-SSA-LSTM Driving force Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 19 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviewers invited by journal 03 Jun, 2024 Editor assigned by journal 03 Jun, 2024 Editor invited by journal 30 May, 2024 Submission checks completed at journal 27 May, 2024 First submitted to journal 24 May, 2024 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-4474949","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310890453,"identity":"ae6f0812-7576-43d9-a373-1b507b492b32","order_by":0,"name":"Fan Yang","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""},{"id":310890454,"identity":"e12f8a3d-cccd-460b-a3ae-ceff42ebabcf","order_by":1,"name":"Menghui Zhi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBAC+xmMDQyMDTY8/PwNRGphhGhJk5GccYBoLSCi4bCNQUMCkVqYpZsbmG7uOM9jwHCA8cPHHCK0sMkcbGDOPXObx5y5gVly5jYitPBIJAK1tN3msWw4wMbMS4wWCYiWczwGBxKI1GIA0XKAdC3JPJIzDjYT5xf7GekPgFrs7Pn5mw9++EiMFiBg/wGhgXE6CkbBKBgFo4BKAAA4yzQPsqv+egAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Menghui","middleName":"","lastName":"Zhi","suffix":""},{"id":310890456,"identity":"8bd2aa65-bcca-4b48-89af-3c0aa13c3f9d","order_by":2,"name":"Yan An","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"An","suffix":""}],"badges":[],"createdAt":"2024-05-25 03:24:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4474949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4474949/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88524-0","type":"published","date":"2025-02-17T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77052615,"identity":"3faa18c0-be2e-4f7c-975c-6890dfeefc3e","added_by":"auto","created_at":"2025-02-24 16:18:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1220771,"visible":true,"origin":"","legend":"","description":"","filename":"RevealingLargeScaleSurfaceSubsidenceinJinchengCitysMiningClustersUsingMTInSARandVMDSSALSTMTimeSeriesPredictionModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4474949/v1_covered_bec4f184-4a7e-4dbf-ac4c-923365d4d766.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Surface Subsidence, MT-InSAR, VMD-SSA-LSTM, Driving force","lastPublishedDoi":"10.21203/rs.3.rs-4474949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4474949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJincheng City's mining areas have long been plagued by surface subsidence, posing significant threats to local residents' safety and impacting the region's economic and social stability. Understanding and effectively monitoring the driving factors and mechanisms of surface subsidence are crucial for devising scientific prevention measures and promoting the sustainable development of mining areas. This article aims to comprehensively reveal the large-scale surface subsidence phenomenon in Jincheng City's mining clusters by utilizing advanced remote sensing technology and machine learning models, identifying its main driving forces, and predicting future subsidence trends to provide scientific evidence for geological disaster prevention in mining areas. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, using both Permanent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset Interferometric SAR (SBAS-InSAR) techniques for cross-validation, to confirm the existence of surface subsidence. Further, by integrating Variational Mode Decomposition (VMD), Singular Spectrum Analysis (SSA), and Long Short-Term Memory (LSTM) networks, a high-precision time series prediction model (VMD-SSA-LSTM) was developed. The results indicate that from 2018 to 2021, the surface subsidence rates in Jincheng City ranged from \u0026minus;\u0026thinsp;34 to 34 millimeters per year, with significant variations in subsidence levels across different areas. Gaoping City exhibited the highest subsidence, with rates ranging from \u0026minus;\u0026thinsp;34 to 5 mm per year, while Yangcheng County showed the most pronounced subsidence changes. These variations are primarily attributed to mining activities, land use changes, and adverse geological conditions in Jincheng City. This study unveils the large-scale surface subsidence phenomenon in Jincheng City's mining clusters, marking the first comprehensive ground deformation monitoring analysis of small mining clusters across four cities in Jincheng. The development of a high-precision surface subsidence prediction model provides new insights for scientifically understanding geological disasters in mining areas. These findings are significant for formulating effective geological disaster prevention measures and land management policies.\u003c/p\u003e","manuscriptTitle":"Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 10:47:11","doi":"10.21203/rs.3.rs-4474949/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-23T07:24:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-19T09:12:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T22:14:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168794877796464077576301999170049823637","date":"2024-09-10T07:00:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64334200803421512724763865859709900687","date":"2024-08-30T14:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222528076162312120188927575503127111405","date":"2024-06-08T19:10:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263998747689266994397169001623734864410","date":"2024-06-03T16:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-03T12:03:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-03T12:01:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-30T12:44:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-27T11:12:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-25T03:23:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88210b43-d1c5-4c5d-9abb-ca057fb282d7","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32855655,"name":"Earth and environmental sciences/Environmental sciences"},{"id":32855656,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-02-24T16:02:29+00:00","versionOfRecord":{"articleIdentity":"rs-4474949","link":"https://doi.org/10.1038/s41598-025-88524-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-17 15:57:36","publishedOnDateReadable":"February 17th, 2025"},"versionCreatedAt":"2024-06-07 10:47:11","video":"","vorDoi":"10.1038/s41598-025-88524-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-88524-0","workflowStages":[]},"version":"v1","identity":"rs-4474949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4474949","identity":"rs-4474949","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}