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用于识别氡时间序列中周期性和异常现象的信号分解与分析

SIGNAL DECOMPOSITION AND ANALYSIS FOR THE IDENTIFICATION OF PERIODIC AND ANOMALOUS PHENOMENA IN RADON TIME-SERIES.

作者信息

Sabbarese C, Ambrosino F, De Cicco F, Pugliese M, Quarto M, Roca V

机构信息

Dipartimento di Matematica e Fisica, Università degli Studi della Campania 'Luigi Vanvitelli', Caserta, Italy.

Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Italy.

出版信息

Radiat Prot Dosimetry. 2017 Nov 1;177(1-2):202-206. doi: 10.1093/rpd/ncx159.

Abstract

This work concerns continuous monitoring of radon and thoron specific activity in soil gas within the framework of identifying possible anomalies. It is based on the analysis of a medium-term data record obtained from soil gas in an area of geophysical interest. The RaMonA spectrometric system is also used to measure the climatic parameters and a specific analysis of the alpha spectra is performed to better determine the alpha lines intensity. Since radon emission is also influenced by meteorological parameters, it is mandatory to differentiate the changes due to the deep phenomena. Different procedures are utilized to reach the above objective: statistical analysis using the Empirical Mode Decomposition technique, the Multiple Linear Regression method and the Remote Radon Estimation by using of the thoron trend to eliminate the locally produced radon fraction. The results of such methods are compared to recognize and to highlight radon anomalies.

摘要

这项工作涉及在识别可能异常的框架内对土壤气体中氡和钍射气比活度进行连续监测。它基于对从地球物理感兴趣区域的土壤气体中获得的中期数据记录的分析。RaMonA光谱系统也用于测量气候参数,并对α谱进行特定分析以更好地确定α线强度。由于氡的释放也受气象参数影响,因此必须区分由深部现象引起的变化。采用了不同程序来实现上述目标:使用经验模态分解技术的统计分析、多元线性回归方法以及利用钍射气趋势消除本地产生的氡份额的远程氡估计。比较这些方法的结果以识别和突出氡异常。

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