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Assessment of Rn-222 continuous time series for the identification of anomalous changes during moderate earthquakes of the Garhwal Himalaya.
Applied Radiation and Isotopes ( IF 1.6 ) Pub Date : 2020-07-28 , DOI: 10.1016/j.apradiso.2020.109327
Vaishali Shukla 1 , Vishal Chauhan 1 , Naresh Kumar 1 , Devajit Hazarika 1
Affiliation  

Over years, soil radon (Rn-222) measurement was started at Multi-parametric Geophysical Observatory (MPGO), Ghuttu, Garhwal Himalaya to assess the earthquake precursory signatures. We carried out a statistical analysis to examine anomalous soil radon emanation during the occurrence of local earthquakes. Twenty earthquakes of moderate and bigger magnitude are occurred within 300 km from MPGO, Ghuttu during 2009–2017. Continuous time series highlight a high effect of rainfall precipitation on the soil radon emanation measured at 10 m depth. During monsoon period (June to September), high rain precipitation at the recording site cause a high variation in the radon emanation. In spite of our best efforts, it is difficult to isolate the complex behavior of heavy and abrupt occurrence of rain from the soil radon data. Preferably, the data of 11 events of the pre- and post-monsoon are evaluated to identify the seismic origin effect on soil radon. To examine these anomalous variations, statistical analysis of soil radon data is carried out by calculating mean (m), and then obtaining standard deviation (σ) from mean values. Changes in the soil radon concentration are treated anomalous for values exceeding one and two standard deviations (m±σ and m±2σ) from the mean value for the selected duration. Two nearby and the strongest events suggests pre-seismic variations which are related to the earthquake precursory signatures. The observed results are explained in the light of dilatancy- diffusion model.



中文翻译:

评估Rn-222连续时间序列,以识别Garhwal喜马拉雅山中度地震期间的异常变化。

多年来,在Garhwal喜马拉雅山Ghuttu的多参数地球物理天文台(MPGO)上开始进行土壤ra(Rn-222)测量,以评估地震前兆特征。我们进行了统计分析,以检查当地地震发生期间土壤soil的异常散发。2009-2017年,距古特MPGO 300公里以内发生了20次中度和较大地震。连续时间序列突显了降雨降水对10 m深度处土壤ra释放的影响。在季风期(6月至9月),记录地点的高降雨降水导致ra气散发变化很大。尽管我们已尽了最大努力,但很难从土壤ra数据中分离出大雨突然发生的复杂行为。最好是 评估了季风前后前后11个事件的数据,以确定地震起源对土壤ra的影响。为了检查这些异常变化,通过计算平均值(m),然后从平均值中获得标准偏差(σ),对土壤data数据进行统计分析。在选定的持续时间内,如果土壤from浓度与平均值之间的差值超过一两个标准差(m±σ和m±2σ),则将其异常处理。附近的两个最强烈的事件表明与地震先兆特征有关的震前变化。根据膨胀-扩散模型解释了观察到的结果。通过计算平均值(m),然后从平均值中获得标准偏差(σ),对土壤ra数据进行统计分析。在选定的持续时间内,如果土壤from浓度与平均值之间的差值超过一两个标准差(m±σ和m±2σ),则将其异常处理。附近的两个最强烈的事件表明与地震先兆特征有关的震前变化。根据膨胀-扩散模型解释了观察到的结果。通过计算平均值(m),然后从平均值中获得标准偏差(σ),对土壤ra数据进行统计分析。在选定的持续时间内,如果土壤from浓度与平均值之间的差值超过一两个标准差(m±σ和m±2σ),则将其异常处理。附近的两个最强烈的事件表明与地震先兆特征有关的震前变化。根据膨胀-扩散模型解释了观察到的结果。附近的两个最强烈的事件表明与地震先兆特征有关的震前变化。根据膨胀-扩散模型解释了观察到的结果。附近的两个最强烈的事件表明与地震先兆特征有关的震前变化。根据膨胀-扩散模型解释了观察到的结果。

更新日期:2020-07-28
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