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Quasi-Real-Time Inversion of Large Earthquake Rupture Process Based on Earthquake Early Warning System in China: Applications to 2017 MW 6.5 Jiuzhaigou and 2022 MW 6.7 Menyuan Earthquakes.

  • Published In: Journal of Earthquake & Tsunami, 2024, v. 18, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Jie-Min; Liang, Yuan-Yuan; Wu, Zhe; Pu, Ju; Li, Yonghong; Ma, Pifeng; Wang, Fengji; Yin, Haitao 3 of 3

Abstract

The study of seismic ruptures is crucial for understanding major earthquake events. Historically, research on large earthquake rupture processes has required a long time to be published. However, recent advancements in earthquake early warning (EEW) systems have allowed for more rapid analyses of earthquake rupture inversions. The widespread and dense distribution of EEW stations provides in China an opportunity to study and invert rupture processes in near-real time. Two notable earthquakes were studied using this technology: the 2017 Jiuzhaigou MW 6.5 earthquake in Sichuan Province, China, and the 2022 Menyuan MW 6.7 earthquake in Qinghai Province, China. In both instances, numerous strong-motion sensors captured the seismic events and transmitted waveform data to data analysis centers in real time. Following automated site selection and preparation procedures, the rupture processes of these earthquakes were analyzed and released within 30 min of the event origin. This rapid response time demonstrates that the seismic rupture process determined through inversion using existing EEW systems can serve as a guide for emergency rescue work after earthquakes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Earthquake & Tsunami. 2024/04, Vol. 18, Issue 2, p1
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2024
  • ISSN:1793-4311
  • DOI:10.1142/S1793431123500380
  • Accession Number:176408319
  • Copyright Statement:Copyright of Journal of Earthquake & Tsunami is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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