Radon in soil gas: How to identify anomalies caused by earthquakes
Introduction
Since the advent of the nuclear era in Slovenia in the sixties, 222Rn and other radionuclides have been systematically monitored in ground and surface waters (Kobal et al., 1978, Kobal et al., 1990, Kobal, 1979, Kobal and Renier, 1987, Kobal and Fedina, 1987, Vaupotič and Kobal, 2001, Vaupotič, 2002, Popit et al., 2002, Popit et al., 2004). The first Rn analyses with the objective of forecasting earthquakes (Ulomov and Mavashev, 1971, Scholz et al., 1973, Mjachkin et al., 1975, King, 1978, King, 1986, Ui et al., 1988, Ohno and Wakita, 1996, Pulinets et al., 1997, Toutain and Baubron, 1999, Planinić et al., 2000, Planinić et al., 2001, Belyaev, 2001, Virk et al., 2001) were carried out in Slovenia in 1982 (Zmazek et al., 2000a). Radon concentrations were determined weekly in 4 thermal water springs, while Cl−, , hardness and pH, were determined monthly. In 1998, this study was extended to other thermal water springs (Zmazek et al., 2000b, Zmazek et al., 2002a, Zmazek et al., 2002b) and also to soil gas (Zmazek et al., 2000c, Zmazek et al., 2002c) at selected, seismically relevant sites, and sampling frequency was increased from once a week to once an hour. In this paper the authors will focus on the Rn concentration in soil gas.
Since April 1999, in 60–90 cm deep boreholes at 6 locations in the Krško basin, Rn concentration in soil gas, barometric pressure and soil temperature have been measured and recorded once an hour, using barasol probes (MC-450, ALGADE, France). Other meteorological data, such as air temperature and rainfall, have been provided by the Office of Meteorology of the Environmental Agency of the Republic of Slovenia, and seismic data by the Office of Seismology of the same agency. Boreholes 1 and 4 are located in the Orlica fault zone, at a distance about 4000 m from each other, while the other boreholes are at distances from 150 to 2500 m on either side of the fault zone (Fig. 1). Air temperature and rainfall were measured at the meteorological station Bizeljsko, approximately 14 km from the boreholes. This paper interprets data only from stations 1 (Krško-1), 5 (Kremen) and 6 (Grmada), because measurements were often interrupted and disturbed at the others. The experimental procedure is reported elsewhere (Zmazek et al., 2002c). As often utilised, for earthquakes Dobrovolsky’s equation (Dobrovolsky et al., 1979) was used to calculate RD i.e., RD = 100.43M, where M is the earthquake magnitude and RD the radius of the zone within which precursory phenomena may be manifested (so-called Dobrovolsky’s radius in km). Earthquakes for which the distance RE between the epicentre and the measuring site was equal or less than 2RD have been used in the interpretation.
Following general practice in this field (Yasuoka and Shinogi, 1997, Singh et al., 1999, Virk et al., 2001), an anomaly in Rn concentration is defined as a significant deviation from the mean value and is then related to seismic activity (Zmazek et al., 2002c). It is often impossible to distinguish an anomaly caused solely by a seismic event, from one resulting from meteorological or hydrological parameters. For this reason, the implementation of more advanced statistical methods in data evaluation (Di Bello et al., 1998, Cuomo et al., 2000, Biagi et al., 2001, Belyaev, 2001, Negarestani et al., 2001, Planinić et al., 2003, Steinitz et al., 2003) is important. In this paper, in addition to Rn anomalies expressed as deviations of Rn concentration from the average seasonal value by more than a multiple of standard deviation, and those based on a positive correlation between the time gradients of barometric pressure and of Rn concentration, the authors have applied regression trees for the first time to forecast earthquakes. Data mining and machine learning methods used for that purpose have been already successfully applied to many environmental problems, as reviewed by Džeroski (2002). Here, Rn concentrations are predicted on the basis of environmental data (air and soil temperature, barometric pressure, rainfall) during seismically non-active periods, and then is tested the hypothesis that the prediction is significantly worsened during seismically active periods is tested.
Section snippets
Methodology of data analysis
Experimental data have been analysed by searching for Rn anomalies (i) defined as deviations in Rn concentration of more than ±×σ (multiple × of standard deviation σ), from the average seasonal value (ii) expressed as those time intervals when time gradients of barometric pressure and Rn concentration have the same sign, and (iii) by using regression trees, which have been confirmed as outperforming other regression methods in this area (Zmazek et al., 2003).
Results and discussion
Experimental results are shown in Fig. 2. In the following sections, these raw data will be analysed by applying the approaches mentioned above. It will be seen that some earthquakes are preceded and accompanied by Rn anomalies (denoted as CA case: correct anomaly related to seismic events), some are not (denoted as NA case: no anomaly observed for an earthquake), and, also, that there are anomalies during seismically non-active periods (denoted as FA case: false anomaly appearing without a
Conclusions
The analysis has shown that Rn anomalies (i) based on deviations of Rn concentration from the seasonal average concentration have been less successful in predicting earthquakes as those (ii) based on the time gradient of Rn concentration and barometric pressure having simultaneously the same sign, and (iii) obtained with model trees. Approaches (i) and (ii) strongly depend on the values of ±×σ and ΔP/Δt thresholds, respectively, while the dependence of approach (iii) on the threshold of (CRn)m/(
Acknowledgements
The study was funded by the Slovenian Ministry of Education, Science and Sport. The authors thank Prof. H. Ui from the Toyama University, Japan, for fruitful discussions and his constructive suggestions.
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2019, Applied Radiation and Isotopes