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Groundwater radon precursor anomalies identification by decision tree method

地下水 异常(物理) 地质学 地震学 水文学(农业) 土壤科学 环境科学 岩土工程 凝聚态物理 量子力学 物理
作者
Yao-Zhong Zhang,Zheming Shi,Guangcai Wang,Rui Yan,Zuochen Zhang
出处
期刊:Applied Geochemistry [Elsevier BV]
卷期号:121: 104696-104696 被引量:32
标识
DOI:10.1016/j.apgeochem.2020.104696
摘要

Radon in groundwater has long been recognized as a sensitive indicator of crustal stress. Significant changes in groundwater radon concentration before earthquakes have been documented in many studies. However, the radon concentration in groundwater may be affected by many interference factors. The anomalies before seismic activities may not be large enough to be clearly identified by the conventional statistical method. Therefore, new methods are needed to identify the possible pre-seismic anomalies. In this study, we investigated 38 years’ worth of radon time series data (1977–2015) in a hot spring to identify the possible precursor anomalies. We first identify the factors that may affect the radon fluctuations by wavelet coherence analysis, spring discharge, water temperature, rainfall and barometric pressure. All are found to be closely related to the radon fluctuation. The time series (1980–2008) were used for further decision tree analysis as a high correlation in the duration. Following this, we constructed the decision tree models based on these factors to model the “background” radon fluctuation and identify the anomalies by comparing the difference between the observed radon changes and the “background” fluctuations. The modeled “background” fluctuation is closely related to the observed data during the non-seismic activity period, with the correlation coefficient of 0.8. Following this, we compared the modeled “background” fluctuation of the radon time series with the observed one during the seismic activities period. The decision tree could identify 15 possible radon anomalies among the 24 chosen earthquakes. The identified anomalies are also supported by the anomaly changes in water temperature and spring discharge. Therefore, we believe that the decision tree method could be an efficient way to identify the possible precursor anomalies in future studies. Additionally, we explore the mechanism of radon anomalies. The plausible mechanism for the anomalous increase is that radon is continuously supplied from newly formed internal surfaces of the crack to the aquifer system. For the anomalous decrease, it might be related to radon partitioning into the gas phase and the change of mixing ratio of shallow and depth water.

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