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Near-epicenter-based partial matching crossover algorithm for estimating the strong-shaking zone of large earthquakes.

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Title: Near-epicenter-based partial matching crossover algorithm for estimating the strong-shaking zone of large earthquakes.
Authors: Wang, Yuan1 (AUTHOR) wangyuanseis@gmail.com, Colombelli, Simona2 (AUTHOR), Zollo, Aldo2 (AUTHOR), Li, Shanyou3 (AUTHOR)
Source: Bulletin of Earthquake Engineering. Sep2024, Vol. 22 Issue 11, p5545-5570. 26p.
Subject Terms: *Earthquake magnitude, *Ground motion, *Earthquake prediction, *Earthquake zones, *Lead time (Supply chain management)
Abstract: The rapid and accurate prediction of earthquake Strong-Shaking Zone (SSZ) is crucial for issuing precise early warnings to regions at high risk of strong ground shaking. Generally, the SSZ is derived from the real-time spatial distribution of observed ground motions. However, during the initial stages of large earthquakes, the SSZ is often underestimated and provide alerts without enough lead-time (the time interval between the alert declaration and the S-wave arrival to the target area). In this study, we propose an innovative approach termed Near-epicenter-based Partial Matching Crossover. Leveraging the characteristic that reliable magnitude estimates for large earthquakes are available earlier than accurate predictions of the peak ground velocity (PGV) distribution, this approach utilizes near-epicenter station data to rapidly estimate the SSZ. It achieves this by matching a segment of the fault, defined by a predetermined length, with the predicted PGV map within a 120 km radius centered at the epicenter. Application of our method to strong motion data from China, Japan and Turkey demonstrates its efficacy in quickly anticipating the post-earthquake intensity distributions for large earthquakes. Specifically, it offers a lead time of 5 s or more for 51.5% (39,354 km2), 43.3% (5772 km2), 31%(47,107 km2) and 75.3% (81,966 km2) of the IMM = V region during the M 8 Wenchuan earthquake, the M 7.3 Kumamoto earthquake, the M 7.8 Syria earthquake and M 7.6 Turkey earthquake, respectively. The presented approach introduces a novel methodology to extend the lead time for earthquake early warnings. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
An: 179573360
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  Label: Title
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  Data: Near-epicenter-based partial matching crossover algorithm for estimating the strong-shaking zone of large earthquakes.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yuan%22">Wang, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangyuanseis@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Colombelli%2C+Simona%22">Colombelli, Simona</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zollo%2C+Aldo%22">Zollo, Aldo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shanyou%22">Li, Shanyou</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Bulletin+of+Earthquake+Engineering%22">Bulletin of Earthquake Engineering</searchLink>. Sep2024, Vol. 22 Issue 11, p5545-5570. 26p.
– Name: Subject
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  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Earthquake+magnitude%22">Earthquake magnitude</searchLink><br />*<searchLink fieldCode="DE" term="%22Ground+motion%22">Ground motion</searchLink><br />*<searchLink fieldCode="DE" term="%22Earthquake+prediction%22">Earthquake prediction</searchLink><br />*<searchLink fieldCode="DE" term="%22Earthquake+zones%22">Earthquake zones</searchLink><br />*<searchLink fieldCode="DE" term="%22Lead+time+%28Supply+chain+management%29%22">Lead time (Supply chain management)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The rapid and accurate prediction of earthquake Strong-Shaking Zone (SSZ) is crucial for issuing precise early warnings to regions at high risk of strong ground shaking. Generally, the SSZ is derived from the real-time spatial distribution of observed ground motions. However, during the initial stages of large earthquakes, the SSZ is often underestimated and provide alerts without enough lead-time (the time interval between the alert declaration and the S-wave arrival to the target area). In this study, we propose an innovative approach termed Near-epicenter-based Partial Matching Crossover. Leveraging the characteristic that reliable magnitude estimates for large earthquakes are available earlier than accurate predictions of the peak ground velocity (PGV) distribution, this approach utilizes near-epicenter station data to rapidly estimate the SSZ. It achieves this by matching a segment of the fault, defined by a predetermined length, with the predicted PGV map within a 120 km radius centered at the epicenter. Application of our method to strong motion data from China, Japan and Turkey demonstrates its efficacy in quickly anticipating the post-earthquake intensity distributions for large earthquakes. Specifically, it offers a lead time of 5 s or more for 51.5% (39,354 km2), 43.3% (5772 km2), 31%(47,107 km2) and 75.3% (81,966 km2) of the IMM = V region during the M 8 Wenchuan earthquake, the M 7.3 Kumamoto earthquake, the M 7.8 Syria earthquake and M 7.6 Turkey earthquake, respectively. The presented approach introduces a novel methodology to extend the lead time for earthquake early warnings. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10518-024-01981-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 5545
    Subjects:
      – SubjectFull: Earthquake magnitude
        Type: general
      – SubjectFull: Ground motion
        Type: general
      – SubjectFull: Earthquake prediction
        Type: general
      – SubjectFull: Earthquake zones
        Type: general
      – SubjectFull: Lead time (Supply chain management)
        Type: general
    Titles:
      – TitleFull: Near-epicenter-based partial matching crossover algorithm for estimating the strong-shaking zone of large earthquakes.
        Type: main
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          Name:
            NameFull: Wang, Yuan
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            NameFull: Colombelli, Simona
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            NameFull: Zollo, Aldo
      – PersonEntity:
          Name:
            NameFull: Li, Shanyou
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          Dates:
            – D: 01
              M: 09
              Text: Sep2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 1570761X
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              Value: 22
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Bulletin of Earthquake Engineering
              Type: main
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