基于人工神经网络的仪器地震烈度预测模型研究
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李水龙(1988-),男,硕士,工程师,研究方向为地震预警与烈度速报技术研究与系统研发。E-mail:lisl@fjea.gov.cn

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国家重点研发计划(2018YFC1504005);国家重点研发计划(2017YFC1508002)


Prediction models for instrumental seismic intensity based on artificial neural network
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    摘要:

    地震发生后,针对能够快速预测震中附近的烈度分布情况的问题,首先对632次地震触发的台站进行筛选,对2 231个台站触发后20s内有效的7个地震动参数以及震级和震源距的信息进行提取,并利用人工神经网络对所选数据样本进行训练,建立三种有效的预测模型。研究结果显示模型一所选的输入参数为7个,不利用震源参数,在预测中有着较好的时效性,从第1s到20s,预测的平均烈度差值逐渐减小到0.45;模型二所选的输入参数为8个,利用了震源距信息,可以用于烈度级别的预测,预测的平均烈度差值逐渐减小到0.36;模型三所选的输入参数为9个,预测结果较好,可用于震后烈度场的实时预测,平均烈度差值逐渐减小到0.31.利用提出的3种模型对两次地震事件进行烈度预测,预测烈度差值取整后分别有95%和76%以上在1以内,有着较好的结果,可以用于地震预警当中。

    Abstract:

    To predict the distribution of instrumental seismic intensities around the epicenter immediately after an earthquake, we selected 2 331 triggered strong motion stations from 632 earthquakes and extracted the epicentral distance, magnitude, and seven effective ground motion parameters within 20 seconds after the triggers to the stations. The selected data samples were trained using an artificial neural network, and three effective prediction models were established. The results show the following:(1)Model one, which selects seven ground motion parameters without epicentral distance and magnitude, has good timeliness in prediction. From 1 to 20 s, the average intensity difference in prediction gradually reduces to 0.45.(2)Model two selects eight ground motion parameters, including the epicentral distance; it can be used to ensure early earthquake warning through the prediction of magnitude. The average intensity difference in prediction gradually reduces to 0.36.(3)Model three selects nine ground motion parameters and has the best prediction result among the three models; its average intensity difference in prediction reduces to 0.31, and it can be used to predict the post earthquake intensity field in real time. The three models were used to predict the intensity of two real earthquakes, and the difference in predicted intensity is greater than 95% and 76%, within 1, indicating that the models can be applied in earthquake early warning.

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李水龙,陈以伦,于伟恒,周施文.基于人工神经网络的仪器地震烈度预测模型研究[J].地震工程学报,2023,(1):181-190. LI Shuilong, CHEN Yilun, YU Weiheng, ZHOU Shiwen. Prediction models for instrumental seismic intensity based on artificial neural network[J]. China Earthquake Engineering Journal,2023,(1):181-190.

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  • 在线发布日期: 2023-01-21
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