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<p>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&#xa0;s. The model incorporates five key parameters: magnitude, epicentral distance, site conditions (represented by the predominant period <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11600_2025_1597_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\({T}_{0}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Jianwen Cui,
  • Dawei Lu,
  • Shuo Xu,
  • Guoliang Lin,
  • Yahong Shen,
  • Zhihao Cui

摘要

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 \({T}_{0}\) 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.