赵庆旭, 王延伟, 莫红艳, 曹振中. 0: 基于多输入高斯过程回归的震级快速估算方法研究. 地震学报. doi: 10.11939/jass.20220223
引用本文: 赵庆旭, 王延伟, 莫红艳, 曹振中. 0: 基于多输入高斯过程回归的震级快速估算方法研究. 地震学报. doi: 10.11939/jass.20220223
Qingxu ZHAO, YanWei WANG, Hongyan MO, ZhenZhong CAO. 0: Rapid magnitude estimation based on multi-input Gaussian process regression. Acta Seismologica Sinica. doi: 10.11939/jass.20220223
Citation: Qingxu ZHAO, YanWei WANG, Hongyan MO, ZhenZhong CAO. 0: Rapid magnitude estimation based on multi-input Gaussian process regression. Acta Seismologica Sinica. doi: 10.11939/jass.20220223

基于多输入高斯过程回归的震级快速估算方法研究

Rapid magnitude estimation based on multi-input Gaussian process regression

  • 摘要: 快速准确地估算震级对地震预警有着极其重要的意义。基于初至地震波某一特征参数的震级估算方法被广泛应用于地震预警中,但是由单一特征参数建立的震级经验公式,并不能充分利用初至地震波中与震级相关的信息,极大地制约了震级估算的效果。为此,本文提出将初至地震波时域、频域和时频域的10个特征参数输入高斯过程回归估算震级(GPR-M方法)。利用日本的大量地表强震记录对GPR-M进行训练和测试,并与最大卓越周期τpmax方法和位移幅值Pd方法进行对比。结果表明,GPR-M在有无震源距离两种情况下,估算震级的准确性均显著好于τpmax方法和Pd方法。此外,在利用智利的地表强震记录对日本数据训练的GPR-M进行泛化能力测试的结果表明,GPR-M比τpmax方法和Pd方法具有更好的泛化能力。本文提出的GPR-M可以有效改善EEWs估算震级的准确性且不受地域差异的影响。

     

    Abstract: Fast and accurate magnitude estimation is essential for earthquake early warning system(EEWs). The magnitude estimation methods based on a single characteristic parameter of  the initial wave is widely used in EEWs. However, the empirical formula of magnitude established by a single characteristic parameter cannot sufficiently utilize information related to the magnitude in the first arrival wave, which greatly limits the effectiveness of magnitude estimation. this study has proposed a new approach (GPR-M) based on Gaussian process regression to estimate magnitude that includes 10 characteristic parameters in the time domain, frequency domain, and time-frequency domain of initial wave. GPR-M is trained and tested using a large number of surface strong earthquake records in Japan, and compared with the maximum predominant period τpmax method and the peak displacement Pd method. The results show that the accuracy of GPR-M is significantly better than τpmax and Pd in estimating magnitude with and without hypocentral distance in both cases. In addition, the generalization ability test of GPR-M trained by Japanese data using Chile's surface strong motion records shows that GPR-M has better generalization ability than τpmax and Pd. GPR-M can effectively improve the accuracy of magnitude estimation for EEWs without being affected by regional differences..

     

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