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首页> 外文期刊>Seismological research letters >Artificial Neural Network-Based Framework for Developing Ground-Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas
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Artificial Neural Network-Based Framework for Developing Ground-Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas

机译：基于人工神经网络的俄克拉荷马州，堪萨斯州和德克萨斯州自然和诱导地震发展地面运动模型的框架

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摘要

This article puts forward an artificial neural network (ANN) framework to develop ground-motion models (GMMs) for natural and induced earthquakes in Oklahoma, Kansas, and Texas. The developed GMMs are mathematical equations that predict peak ground acceleration, peak ground velocity, and spectral accelerations at different frequencies given earthquake magnitude, hypocentral distance, and site condition. The motivation of this research stems from the recent increase in the seismicity rate of this particular region, which is mainly believed to be the result of the human activities related to petroleum production and wastewater disposal. Literature has shown that such events generally have shallow depths, leading to large-amplitude shaking, especially at short hypocentral distances. Thus, there is a pressing need to develop site-specific GMMs for this region. This study proposes an ANN-based framework to develop GMMs using a selected database of 4528 ground motions, including 376 seismic events with magnitudes of 3 to 5.8, recorded over the 4- to 500-km hypocentral distance range in these three states since 2005. The results show that the proposed GMMs lead to accurate estimations and have generalization capability for ground motions with a range of seismic characteristics similar to those considered in the database. The sensitivity of the equations to predictive parameters is also presented. Finally, the attenuation of ground motions in this particular region is compared with those in other areas of North America.
机译：本文提出了一种人工神经网络（ANN）框架，用于在俄克拉荷马，堪萨斯州，德克萨斯州的自然和诱导地震开发地面运动模型（GMMS）。开发的GMMS是在给定地震幅度，低通距离和现场条件的不同频率下预测峰接地加速度，峰值接地速度和光谱加速度的数学方程。该研究的动机源于该特定区域的近期地震率的增加，主要被认为是与石油生产和废水处理有关的人类活动的结果。文献已经表明，这种事件通常具有浅深度，导致大幅度摇动，尤其是在短斜视距离。因此，需要强制需要为该区域开发特定于站点的GMM。本研究提出了一种基于ANN的框架，用于使用4528个地面运动的所选数据库开发GMMS，包括376个震荡事件，其幅度为3至5.8，从2005年以来，这三个州的4至500公里的次频率距离范围内记录。结果表明，建议的GMMS导致准确的估计，并具有与数据库中考虑的各种地震特性的地面运动的泛化能力。还提出了方程对预测参数的敏感性。最后，将该特定区域的地面运动的衰减与北美其他地区的衰减进行了比较。

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  • 来源
    《Seismological research letters》 |2019年第2appa期|共10页
  • 作者

    Khosravikia Farid; Clayton Patricia; Nagy Zoltan;

  • 作者单位

    Univ Texas Austin Dept Civil Architectural &

    Environm Engn 301E E Dean Keeton St C1700 Austin TX 78712 USA;

    Univ Texas Austin Dept Civil Architectural &

    Environm Engn 301E E Dean Keeton St C1700 Austin TX 78712 USA;

    Univ Texas Austin Dept Civil Architectural &

    Environm Engn 301E E Dean Keeton St C1700 Austin TX 78712 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地震学;
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