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评估2000 - 2010年日本关东地区记录的超低频磁数据中的潜在地震前兆信息:距离和震级依赖性

Assessing the Potential Earthquake Precursory Information in ULF Magnetic Data Recorded in Kanto, Japan during 2000-2010: Distance and Magnitude Dependences.

作者信息

Han Peng, Zhuang Jiancang, Hattori Katsumi, Chen Chieh-Hung, Febriani Febty, Chen Hongyan, Yoshino Chie, Yoshida Shuji

机构信息

Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China.

The Institute of Statistical Mathematics, Tokyo 190-8562, Japan.

出版信息

Entropy (Basel). 2020 Aug 1;22(8):859. doi: 10.3390/e22080859.

Abstract

In order to clarify ultra-low-frequency (ULF) seismomagnetic phenomena, a sensitive geomagnetic network was installed in Kanto, Japan since 2000. In previous studies, we have verified the correlation between ULF magnetic anomalies and local sizeable earthquakes. In this study, we use Molchan's error diagram to evaluate the potential earthquake precursory information in the magnetic data recorded in Kanto, Japan during 2000-2010. We introduce the probability gain () and the probability difference () to quantify the forecasting performance and to explore the optimal prediction parameters for a given ULF magnetic station. The results show that the earthquake predictions based on magnetic anomalies are significantly better than random guesses, indicating the magnetic data contain potential useful precursory information. Further investigations suggest that the prediction performance depends on the choices of the distance () and size of the target earthquake events (). Optimal and are about (100 km, 10) and (180 km, 10) for Seikoshi (SKS) station in Izu and Kiyosumi (KYS) station in Boso, respectively.

摘要

为了阐明超低频(ULF)地震磁现象,自2000年起在日本关东地区安装了一个灵敏的地磁网络。在先前的研究中,我们已经验证了超低频磁异常与当地大规模地震之间的相关性。在本研究中,我们使用莫尔昌误差图来评估2000 - 2010年期间日本关东地区记录的磁数据中的潜在地震前兆信息。我们引入概率增益()和概率差()来量化预测性能,并探索给定超低频地磁台站的最佳预测参数。结果表明,基于磁异常的地震预测明显优于随机猜测,这表明磁数据包含潜在的有用前兆信息。进一步的研究表明,预测性能取决于目标地震事件的距离()和规模()的选择。伊豆的清志(SKS)台站和房总的清澄(KYS)台站的最佳和分别约为(100公里,10)和(180公里,10)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf66/7517461/3be4177476de/entropy-22-00859-g001.jpg

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