Estimating S-wave Amplitude for Earthquake Early Warning in New Zealand: Leveraging the First 3 Seconds of P-Wave

Estimating S-wave Amplitude for Earthquake Early Warning in New Zealand: Leveraging the First 3 Seconds of P-Wave | 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 Estimating S-wave Amplitude for Earthquake Early Warning in New Zealand: Leveraging the First 3 Seconds of P-Wave Chanthujan Chandrakumar, Marion Lara Tan, Caroline Holden, Max Stephens, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4475416/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jul, 2024 Read the published version in Earth Science Informatics → Version 1 posted 13 You are reading this latest preprint version Abstract This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ. earthquake early warning low-cost seismometers the PLUM MEMS warning systems S-wave estimation earthquake resilience earthquake detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Jul, 2024 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 12 Jun, 2024 Reviews received at journal 12 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviews received at journal 10 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers invited by journal 06 Jun, 2024 Editor assigned by journal 06 Jun, 2024 Submission checks completed at journal 30 May, 2024 First submitted to journal 25 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. 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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-4475416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312939988,"identity":"7d03472e-92a6-473e-87f1-77d4d1f44860","order_by":0,"name":"Chanthujan 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