Spatiotemporal Variations in Statistical Properties of Ambient Seismic Noise Revealed by Fluctuation-Based Indicators

Spatiotemporal Variations in Statistical Properties of Ambient Seismic Noise Revealed by Fluctuation-Based Indicators | 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 Spatiotemporal Variations in Statistical Properties of Ambient Seismic Noise Revealed by Fluctuation-Based Indicators Hiroyuki Kikuchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9481900/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract We present a quantitative framework for analyzing long-term statistical properties of ambient seismic noise using fluctuation-derived indicators. The approach is based on ground-velocity fluctuation (GVF) time series, from which A-noise is extracted to characterize spectral convexity in a specified frequency band. Two complementary statistical measures are employed: the goodness-of-fit to the Gumbel distribution ( R2 ) derived from extreme value analysis, and the fractal dimension ( D B ) based on a box-counting approach. Application of the method to continuous seismic records from 2006 to 2018 reveals a distinct peak in R2 -SMA prior to the 2011 Mw 9.0 Great East Japan Earthquake (GEJE), preceded by a reduction in D B . These features exhibit a temporal offset of approximately 258 days. In addition, multi-station synchronization of R2 -SMA peaks is identified, including a prolonged interval involving up to six stations. The spatial distribution of synchronized stations evolves over time and shows periods of increased clustering, as quantified by a distance-weighted synchronization metric (NSD). The analysis is conducted independently of earthquake occurrence data, and the GEJE is used solely as a temporal reference within the observation window. The results demonstrate that fluctuation-based statistical indicators can capture coherent spatiotemporal variations in ambient seismic noise under nonstationary conditions. The proposed framework provides a reproducible approach for detecting and characterizing statistical regime changes in long-term seismic noise records. Further work is required to assess the robustness and generality of the observed features across different regions and time periods. Ambient seismic noise Extreme value analysis Statistical synchronization Fractal dimension Spatiotemporal variability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Submission checks completed at journal 24 Apr, 2026 First submitted to journal 21 Apr, 2026 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-9481900","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629228671,"identity":"4d08b39a-7951-4dc3-92d8-5480640bc43c","order_by":0,"name":"Hiroyuki Kikuchi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie2QsUoDQRCGZwhsmonXjiD6CnccXBTk7lVyHJyNjxDk5GCrBNuAeZCUFxbOZmNewEIQrCxMZ6e7GgxC1qQU2a/ZH5aPmX8APJ4/SEhYm6ehgIQArBgOgL6+sNqhHI7WititQMcqEOpPBTaKiz6pOqbhw1Gs2/IRZ6ep7I7nJpxD53b7mLPxdV1Q+0zJQt6FqLmQdF+YUAJOm+2LLbFWvUpRsuxKRsmF4MvEBAU4GfyuxDcbpf+G8t2tLMxiVgl7srVKaqcAysat6HkdTVtFbOpzrnkgTBfOZUHOLvriiV+GKgtGZcKr2VUWmIu9rmR6HDku9hOzSV6tA0WTPQxL9p1OeE/F4/F4/jsfJAhbrsPzoxkAAAAASUVORK5CYII=","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Kikuchi","suffix":""}],"badges":[],"createdAt":"2026-04-21 09:23:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9481900/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9481900/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107923483,"identity":"0b6caeac-2a81-4fe4-9d98-b8b73914a104","added_by":"auto","created_at":"2026-04-27 15:12:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1738025,"visible":true,"origin":"","legend":"","description":"","filename":"texKikuchi21apr2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9481900/v1_covered_958d8ccf-10ac-414c-aafb-8d7875da9b09.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Variations in Statistical Properties of Ambient Seismic Noise Revealed by Fluctuation-Based Indicators","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"journal-of-seismology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jose","sideBox":"Learn more about [Journal of Seismology](http://link.springer.com/journal/10950)","snPcode":"10950","submissionUrl":"https://submission.nature.com/new-submission/10950/3","title":"Journal of Seismology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ambient seismic noise, Extreme value analysis, Statistical synchronization, Fractal dimension, Spatiotemporal variability","lastPublishedDoi":"10.21203/rs.3.rs-9481900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9481900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe present a quantitative framework for analyzing long-term statistical properties of ambient seismic noise using fluctuation-derived indicators. The approach is based on ground-velocity fluctuation (GVF) time series, from which A-noise is extracted to characterize spectral convexity in a specified frequency band. Two complementary statistical measures are employed: the goodness-of-fit to the Gumbel distribution (\u003cem\u003eR2\u003c/em\u003e) derived from extreme value analysis, and the fractal dimension (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e) based on a box-counting approach.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplication of the method to continuous seismic records from 2006 to 2018 reveals a distinct peak in \u003cem\u003eR2\u003c/em\u003e-SMA prior to the 2011 Mw 9.0 Great East Japan Earthquake (GEJE), preceded by a reduction in D\u003csub\u003eB\u003c/sub\u003e. These features exhibit a temporal offset of approximately 258 days. In addition, multi-station synchronization of \u003cem\u003eR2\u003c/em\u003e-SMA peaks is identified, including a prolonged interval involving up to six stations. The spatial distribution of synchronized stations evolves over time and shows periods of increased clustering, as quantified by a distance-weighted synchronization metric (NSD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis is conducted independently of earthquake occurrence data, and the GEJE is used solely as a temporal reference within the observation window. The results demonstrate that fluctuation-based statistical indicators can capture coherent spatiotemporal variations in ambient seismic noise under nonstationary conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proposed framework provides a reproducible approach for detecting and characterizing statistical regime changes in long-term seismic noise records. 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