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地表震颤瞬时振幅最大值的预后特性

Prognostic Properties of Instantaneous Amplitudes Maxima of Earth Surface Tremor.

作者信息

Lyubushin Alexey, Rodionov Eugeny

机构信息

Institute of Physics of the Earth RAS, Moscow 123242, Russia.

出版信息

Entropy (Basel). 2024 Aug 21;26(8):710. doi: 10.3390/e26080710.

DOI:10.3390/e26080710
PMID:39202181
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11353779/
Abstract

A method is proposed for analyzing the tremor of the earth's surface, measured by GPS, in order to highlight prognostic effects. The method is applied to the analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of the principal components from the clusters, with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes, averaged over the number of clusters and the times of earthquakes in the vicinity of Japan with minimum magnitude thresholds of 5.5 for the time interval 2012-2023. It is shown that the sequence of the largest local maxima of instantaneous amplitudes significantly more often precedes the moments of time of earthquakes (roughly speaking, has an "influence") than the reverse "influence" of earthquakes on the maxima of amplitudes.

摘要

提出了一种用于分析通过全球定位系统(GPS)测量的地球表面震颤的方法,以突出预测效果。该方法应用于日本垂直位移的每日时间序列分析。1047个站点的网络被分为15个集群。将黄氏经验模态分解(EMD)应用于集群主成分的时间序列,并随后使用希尔伯特变换计算瞬时振幅。为确保EMD分解波形估计的稳定性,在进行希尔伯特变换之前,对1000个有限幅度白噪声的独立加性实现进行了平均。使用点过程强度的参数模型,我们分析了在2012 - 2023年时间间隔内,日本附近地震最小震级阈值为5.5时,集群数量平均后的瞬时振幅最大局部最大值时间序列与地震时间之间的联系。结果表明,瞬时振幅最大局部最大值序列比地震对振幅最大值的反向“影响”更频繁地先于地震时刻(大致来说,具有“影响”)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/f39909e30992/entropy-26-00710-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/58032d41906c/entropy-26-00710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/ebefcc8ff89e/entropy-26-00710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/fcd5b6200888/entropy-26-00710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/97dfc8674853/entropy-26-00710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/bcca282e081a/entropy-26-00710-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/cdb11b0dc2b2/entropy-26-00710-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/916f0fcb19d2/entropy-26-00710-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/52daa8824b03/entropy-26-00710-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/f39909e30992/entropy-26-00710-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/58032d41906c/entropy-26-00710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/ebefcc8ff89e/entropy-26-00710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/fcd5b6200888/entropy-26-00710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/97dfc8674853/entropy-26-00710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/bcca282e081a/entropy-26-00710-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/cdb11b0dc2b2/entropy-26-00710-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/916f0fcb19d2/entropy-26-00710-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/52daa8824b03/entropy-26-00710-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c8b/11353779/f39909e30992/entropy-26-00710-g009.jpg

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