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在波兰西南部苏台德地区克西翁兹空间研究中心地下地球动力学实验室,对氡应用于识别水管倾斜仪观测到的构造事件进行测试。

Testing of Rn application for recognizing tectonic events observed on water-tube tiltmeters in underground Geodynamic Laboratory of Space Research Centre at Książ (the Sudetes, SW Poland).

作者信息

Przylibski Tadeusz Andrzej, Kaczorowski Marek, Fijałkowska-Lichwa Lidia, Kasza Damian, Zdunek Ryszard, Wronowski Roman

机构信息

Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, Wroclaw, 50-370, Poland.

Space Research Centre, Polish Academy of Sciences, Bartycka 18A, Warsaw, 00-716, Poland.

出版信息

Appl Radiat Isot. 2020 Sep;163:108967. doi: 10.1016/j.apradiso.2019.108967. Epub 2019 Nov 1.

DOI:10.1016/j.apradiso.2019.108967
PMID:32561034
Abstract

Research on relationships between variation in Rn activity concentration and tectonic events recorded using the instruments of the Geodynamic Laboratory of SRC PAS at Książ (the Sudetes, SW Poland) had been conducted since 2014. The performed analyses of variation have demonstrated the spatial character of changes in Rn activity concentration. Their time-course is comparable in all parts of the underground laboratory. This means that gas exchange between the lithosphere and the atmosphere occurs not only through fault zones but also through all surfaces of the underground workings: the floors, the sidewalls and the roofs. Further, some relationships between Rn activity concentration and tectonic activity of the orogen have been demonstrated with the use of Pearson's linear correlation coefficient. The comparison between temporal distribution (times series) of radon activity concentration and water-tube tiltmeters (WTs) demonstrated that radon data have regular oscillations which can be approximated using the sine function with a 12 month cycle (seasonal changes) and amplitude in the range of 1000-1500 Bq/m. To compare the collected radon signal data and tectonic activity, we used linear function as the simplest method of trend assessment. Pearson's correlation coefficient r cannot be accepted as appropriate for assessing the interdependencies between variables because they do not have a normal distribution, and the relationship between them is not linear. It was noted that each series of data, namely radon activity concentration and tectonic activity determine the series of deviations above and below the trend function. Because of the non-fulfillment of the above assumptions, we used nonparametric equivalents such as Spearman's rank correlation coefficient r and Kendall's tau. The obtained results showed that the value of the r coefficient ranges from 0.38 to even 0.43. The best relationship at the level of r = 0.43 was determined between the radon activity concentration recorded by detector no. 3 and the tectonic activity of the rock mass registered on the WT-2 channel. Similar at the r level of 0.37-0.38 between detector no. 5 and 4 and the WT-2 channel. A bit higher than r = 0.39 between detector no. 3 and the WT-2 channel. In each case, these were positive correlations. The obtained Spearman's r coefficients indicate the correlation between Rn activity concentration and tectonic activity of the rock mass. The t-statistic, which analyzes the significance of Spearman's coefficient r is a descriptive measure of the accuracy of regression matching to empirical data. It takes values in the range of percentage and provides informations about which part of the total variability of the radon activity concentration (Y) observed in the sample has been explained (determined) by regression in relation to tectonic activity of the rock mass (X). In our case, approximately f 40% to more than 50% of the radon activity concentration (Y) was explained by regression in relation to the tectonic activity of the rock mass. We obtained similar results with the use of Kendall's tau coefficient. Precise description of the character of this relationship requires further, more detailed analyses, such as comparing characteristics of the distributions based on trend variation like Monte Carlo simulation, Multivariate Adaptive Regression Splines or neural networks.

摘要

自2014年以来,一直在开展关于使用位于克雄日(波兰西南部苏台德地区)的波兰科学院地球动力学实验室的仪器记录的氡(Rn)活度浓度变化与构造事件之间关系的研究。所进行的变化分析表明了Rn活度浓度变化的空间特征。其时间进程在地下实验室的所有区域都是可比的。这意味着岩石圈与大气之间的气体交换不仅通过断层带发生,而且还通过地下工程的所有表面:地面、侧壁和顶板。此外,利用皮尔逊线性相关系数证明了Rn活度浓度与造山带构造活动之间的一些关系。氡活度浓度的时间分布(时间序列)与水管倾斜仪(WTs)之间的比较表明,氡数据具有规则的振荡,可以用周期为12个月(季节变化)、幅度在1000 - 1500 Bq/m范围内的正弦函数来近似。为了比较收集到的氡信号数据和构造活动,我们使用线性函数作为趋势评估的最简单方法。皮尔逊相关系数r不能被认为适合评估变量之间的相互依存关系,因为它们不具有正态分布,并且它们之间的关系不是线性的。值得注意的是,每一系列数据,即氡活度浓度和构造活动,都决定了趋势函数上下的偏差系列。由于上述假设未得到满足,我们使用了非参数等效方法,如斯皮尔曼等级相关系数r和肯德尔tau系数。所得结果表明,r系数的值范围从0.38到甚至0.43。在探测器3记录的氡活度浓度与WT - 2通道记录的岩体构造活动之间确定了r = 0.43水平下的最佳关系。探测器5和4与WT - 2通道之间在r水平为0.37 - 0.38时情况类似。探测器3与WT - 2通道之间略高于r = 0.39。在每种情况下,这些都是正相关。所得到的斯皮尔曼r系数表明了Rn活度浓度与岩体构造活动之间的相关性。分析斯皮尔曼系数r显著性的t统计量是回归与经验数据匹配精度的一种描述性度量。它取值范围为百分比,并提供关于样本中观察到的氡活度浓度(Y)的总变异性的哪一部分已通过相对于岩体构造活动(X)的回归得到解释(确定)的信息。在我们的案例中,相对于岩体构造活动的回归解释了大约40%到超过50%的氡活度浓度(Y)。使用肯德尔tau系数我们也得到了类似的结果。对这种关系特征的精确描述需要进一步更详细的分析,例如基于趋势变化比较分布特征,如蒙特卡罗模拟、多元自适应回归样条或神经网络。

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