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Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.

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Title: Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.
Authors: Cui, Jianwen1,2 (AUTHOR), Lu, Dawei3 (AUTHOR) ludawei@chinarecrm.com.cn, Xu, Shuo2 (AUTHOR), Lin, Guoliang2 (AUTHOR), Shen, Yahong2 (AUTHOR), Cui, Zhihao4 (AUTHOR)
Source: Acta Geophysica. Dec2025, Vol. 73 Issue 6, p5257-5277. 21p.
Subject Terms: *Artificial neural networks, *Ground motion, *Seismograms, *Geographical positions, *Seismology
Geographic Terms: China, Sichuan Sheng (China), Yunnan Sheng (China)
Abstract: This study presents a novel ground motion prediction equation (GMPE) for the Sichuan-Yunnan region of China based on artificial neural networks (ANNs). Utilizing data from 207 earthquake events and 3537 ground motion recordings collected since 2007, the ANN-based GMPEs predict peak ground acceleration (PGA) and pseudo-spectral acceleration (PSA) for periods ranging from 0.04 to 6.0 s. The model incorporates five key parameters: magnitude, epicentral distance, site conditions (represented by the predominant period T 0 derived from horizontal-to-vertical spectral ratios HVSR), hypocentral depth, and styles of faulting. A weighted loss function is employed during ANN training to address data imbalances, particularly the scarcity of near-field recordings. Residual analyses indicate that both inter-event and intra-event variabilities fall within acceptable limits. Comparisons with observed ground motions and existing GMPE commonly used in China confirm that the proposed ANN-based GMPE effectively captures key ground motion characteristics. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 189416137
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Cui%2C+Jianwen%22">Cui, Jianwen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Dawei%22">Lu, Dawei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> ludawei@chinarecrm.com.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Shuo%22">Xu, Shuo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Guoliang%22">Lin, Guoliang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Yahong%22">Shen, Yahong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Zhihao%22">Cui, Zhihao</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Acta+Geophysica%22">Acta Geophysica</searchLink>. Dec2025, Vol. 73 Issue 6, p5257-5277. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Ground+motion%22">Ground motion</searchLink><br />*<searchLink fieldCode="DE" term="%22Seismograms%22">Seismograms</searchLink><br />*<searchLink fieldCode="DE" term="%22Geographical+positions%22">Geographical positions</searchLink><br />*<searchLink fieldCode="DE" term="%22Seismology%22">Seismology</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Sichuan+Sheng+%28China%29%22">Sichuan Sheng (China)</searchLink><br /><searchLink fieldCode="DE" term="%22Yunnan+Sheng+%28China%29%22">Yunnan Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study presents a novel ground motion prediction equation (GMPE) for the Sichuan-Yunnan region of China based on artificial neural networks (ANNs). Utilizing data from 207 earthquake events and 3537 ground motion recordings collected since 2007, the ANN-based GMPEs predict peak ground acceleration (PGA) and pseudo-spectral acceleration (PSA) for periods ranging from 0.04 to 6.0 s. The model incorporates five key parameters: magnitude, epicentral distance, site conditions (represented by the predominant period T 0 derived from horizontal-to-vertical spectral ratios HVSR), hypocentral depth, and styles of faulting. A weighted loss function is employed during ANN training to address data imbalances, particularly the scarcity of near-field recordings. Residual analyses indicate that both inter-event and intra-event variabilities fall within acceptable limits. Comparisons with observed ground motions and existing GMPE commonly used in China confirm that the proposed ANN-based GMPE effectively captures key ground motion characteristics. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=189416137
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11600-025-01597-3
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 5257
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Ground motion
        Type: general
      – SubjectFull: Seismograms
        Type: general
      – SubjectFull: Geographical positions
        Type: general
      – SubjectFull: Seismology
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Sichuan Sheng (China)
        Type: general
      – SubjectFull: Yunnan Sheng (China)
        Type: general
    Titles:
      – TitleFull: Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.
        Type: main
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          Name:
            NameFull: Cui, Jianwen
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            NameFull: Lu, Dawei
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            NameFull: Xu, Shuo
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            NameFull: Lin, Guoliang
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            NameFull: Shen, Yahong
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            NameFull: Cui, Zhihao
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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              Value: 18956572
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              Value: 73
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              Value: 6
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            – TitleFull: Acta Geophysica
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