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利用基于经验模式分解的希尔伯特-黄变换识别印度古吉拉特邦库奇土壤氡-222数据中的地震前兆。

Identification of earthquake precursors in soil radon-222 data of Kutch, Gujarat, India using empirical mode decomposition based Hilbert Huang Transform.

作者信息

Sahoo Sushanta Ku, Katlamudi Madhusudhanarao, Barman Chiranjib, Lakshmi G Udaya

机构信息

Institute of Seismological Research, Gandhinagar, Gujarat, India.

Institute of Seismological Research, Gandhinagar, Gujarat, India.

出版信息

J Environ Radioact. 2020 Oct;222:106353. doi: 10.1016/j.jenvrad.2020.106353. Epub 2020 Aug 9.

Abstract

Soil radon (Rn-222) has been continuously monitored at Badargadh station (23.47°N, 70.62°E) in Kutch region of Gujarat to study the pre-seismic anomalies prior to occurrence of local earthquakes. This monitoring site is in close proximity to the South Wagad Fault, a seismically active fault in the study area. The raw data of radon along with meteorological parameters such as temperature, pressure and humidity in soil of this station for the period of January 01 to December 31, 2017 with a sampling interval of 10 min were used in the analysis. The wind speed and rainfall data of the corresponding period were collected from the nearest weather station. From descriptive statistics, we found an average soil radon concentration of 343 Bq.m. It is observed that radon has a maximum concentration during the rainy season compared to the other two seasons. We found that radon emission rate is less during mid-nights and early morning, whereas, the radon emission is more during afternoon hours when the sun light intensity is more. In order to identify and extract the periodic oscillations in the radon time series, the Empirical Mode Decomposition (EMD) was applied to the soil radon (Rn-222) time series by decomposing it into different oscillatory modes known as the Intrinsic Mode Function (IMF). Several interesting non-linear features emerged from the analysis after applying Hilbert Huang Transform (HHT) on significant IMFs. The temporal variation of the instantaneous energy is well correlated with four local earthquakes during the study period. Most interestingly, intermittencies in the temporal evolution of the instantaneous energy function have been observed prior to these local earthquakes. We present the results of the seismic and aseismic periods as well as a brief discussion of the analysis of radon data which can be used as a precursor of seismic activity. It is now possible to identify anomalies in radon time series using EMD based HHT method even for small-magnitude earthquakes.

摘要

为了研究古吉拉特邦库奇地区局部地震发生前的地震异常情况,在巴达加德站(北纬23.47°,东经70.62°)对土壤氡(Rn - 222)进行了连续监测。该监测点紧邻南瓦加德断层,这是研究区域内一条地震活跃断层。分析使用了该站2017年1月1日至12月31日期间以10分钟为采样间隔的土壤氡原始数据以及温度、压力和湿度等气象参数。相应时期的风速和降雨数据则从最近的气象站收集。通过描述性统计,我们发现土壤氡平均浓度为343 Bq.m。据观察,与其他两个季节相比,氡在雨季的浓度最高。我们发现午夜和清晨时段氡的排放率较低,而在下午阳光强度较大时氡的排放较多。为了识别和提取氡时间序列中的周期性振荡,将经验模态分解(EMD)应用于土壤氡(Rn - 222)时间序列,将其分解为称为本征模态函数(IMF)的不同振荡模式。在对显著的IMF应用希尔伯特 - 黄变换(HHT)后,分析中出现了几个有趣的非线性特征。研究期间,瞬时能量的时间变化与四次局部地震具有良好的相关性。最有趣的是,在这些局部地震之前观察到了瞬时能量函数时间演化中的间歇性。我们展示了地震期和非地震期的结果,并对氡数据的分析进行了简要讨论,这些数据可作为地震活动的前兆。现在,即使对于小震级地震,使用基于EMD的HHT方法也能够识别氡时间序列中的异常。

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