摘要:
为了探索和建立适用于我国的现地峰值加速度（PGA）预测模型，以及提高现地PGA预测的可靠性，本研究提出了一种基于时空图神经网络的现地PGA预测模型（TSGNN-PGA），并采用中国强震数据对TSGNN-PGA模型进行训练和测试。测试结果表明：P波触发后3秒，和Pd-PGA方法相比，TSGNN-PGA模型对于PGA预测有更小的平均绝对误差（MAE）和标准差（STD），以及更大的决定系数，且分别为0.205、0.261和0.688；同时，和Pd-PGA方法相比，在不同的震中距、震级和信噪比范围下，TSGNN-PGA模型对于PGA预测有更小的MAE和STD，这意味着TSGNN-PGA模型对于震中距、震级和信噪比的敏感性更弱，且受影响更小；此外，在积石山6.2级地震中，和Pd-PGA方法相比，TSGNN-PGA模型对于PGA预测表现出更强的鲁棒性。可以推断，TSGNN-PGA模型在一定程度上可以提高我国现地PGA预测的可靠性，且对于地震预警有着重要意义。
Abstract:
In order to explore and establish the onsite peak ground acceleration (PGA) prediction model suitable for China and improve the reliability of onsite PGA prediction, this paper proposes an onsite PGA prediction model based on Temporal-Spatial Graph Neural Network (TSGNN-PGA), and uses the Chinese strong-ground motion records to train and test the TSGNN-PGA model. The test results show that within 3 s after P-wave triggering, compared with the Pd-PGA method, the TSGNN-PGA model has smaller mean absolute error (MAE) and standard deviation (STD), as well as larger coefficient of determination for PGA prediction, with values of 0.205, 0.261, and 0.688, respectively; Meanwhile, compared with the Pd-PGA method, the TSGNN-PGA model has smaller MAE and STD for PGA prediction within different ranges of epicentral distance, magnitude, and signal-to-noise ratio (SNR). This means that the TSGNN-PGA model is less sensitive to epicentral distance, magnitude, and SNR, and is less affected; In addition, compared with the Pd-PGA method, the TSGNN-PGA model shows stronger robustness for PGA prediction in the Jishishan M6.2 earthquake. It can be inferred that the TSGNN-PGA model can improve the reliability of onsite PGA prediction in China to a certain extent, and has important significance for earthquake early warning.
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