Deep-Learning-Based Construction of a High-Resolution Earthquake Catalog for the 2023 Jishishan Ms 6.2 Earthquake Sequence

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Deep-Learning-Based Construction of a High-Resolution Earthquake Catalog for the 2023 Jishishan Ms 6.2 Earthquake Sequence | 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 Deep-Learning-Based Construction of a High-Resolution Earthquake Catalog for the 2023 Jishishan M s 6.2 Earthquake Sequence Shengxia Zhang, Yanping Niu, Zengping Wen, Fengxue Zhang, Jiemin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9102517/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract On 18 December 2023, an \((M_{\mathrm{s}})\) 6.2 earthquake struck Jishishan, Gansu Province, northwestern China. The resulting aftershock sequence provides an important opportunity to investigate the seismogenic structure and postseismic activity in the source region. In this study, we analyzed continuous waveform data recorded by 31 seismic stations within 250 km of the epicenter from 19 to 31 December 2023 to construct a high-resolution earthquake catalog. Seismic phases were automatically detected using the deep-learning phase picker PhaseNet, followed by phase association and preliminary location with the REAL algorithm. Absolute locations were then refined using VELEST, and relative relocations were performed using HypoDD. The resulting catalog contains more than 3200 earthquakes, more than three times the number reported in the routine catalog of the China Earthquake Networks Center (CENC), substantially improving the completeness of the aftershock sequence. The relocated events are mainly distributed at depths of 5--16 km, with an estimated rupture length of approximately 18 km and a width of about 13 km, and they define two major aftershock clusters. Fault-structure analysis based on the E2F program indicates that the aftershocks exhibit a clear NW--SE alignment and are primarily concentrated between the LJS-NF and LJS-SF faults. These observations suggest that the Jishishan earthquake sequence likely ruptured a secondary or previously unmapped fault structure located between the two major mapped faults. The high-resolution earthquake catalog presented here provides new constraints on the detailed structure of the aftershock sequence and contributes to a better understanding of the seismogenic setting of the region. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Jishishan earthquake sequence deep-learning phase picking earthquake catalog Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 19 Mar, 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. 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