GNGTS 2015 - Atti del 34° Convegno Nazionale

GNGTS 2015 S essione 2.1 37 by the seismic network of Friuli Venezia Giulia – OGS, Trieste (OGS, 2002 – 2014) and selected according to Hauksson and Goddard (1981). They formulated the experimental relationship between the magnitude of earthquakes and the maximum distance so that transient phenomena may occur in the fluids circulating in the underground (Anomalies). Shallow earthquakes were used to formulate the relationship. The selected events have to satisfy the following condition: M ≥ 2.4 log 10 D - 0.43 where M is the minimum magnitude required to obtain a radon anomaly at distance D (km). For the anomalies detected before earthquakes, the characteristic parameters were extracted: Amplitude, Precursor Time, and Duration. From the experimental data, theoretical anomalies have been determined in order to verify the possibility to find a correspondence with occurred earthquakes that, in case, could be considered as potentially predictable. For a more accurate evaluation of the data, it was decided to remove from the time series the effects caused by meteorological parameters, such as, in particular, temperature and pressure changes. Since the final objective is a joined interpretation, first among radon data from different sites, and then, among all the observable analysed within the project, homogenization has been carried both of radon data, as of meteorological one, through a daily sampling. Various methods to remove any spurious effects are reported in the literature (Zmazek et al. , 2010; Gregoric et al. , 2012; Gualadini, 2014; Riggio et al. , 2014); the difficulty of finding a standard procedure also derives from the variety of data types and different characteristics of the sites. The first approach to analyse some considered time series was based on the possible occurrence a mathematical correlation between radon and the temperature and pressure values, measured by the instrument itself or in meteorological stations located a few kilometres away. Correlation coefficients never exceeded 0.2, indicating a very poor influence of meteorological parameters. Only in the case of the site of Prato, a strong seasonal component was evident. To this case a methodology was applied which consisted of a cross-correlation computation between the original function and the function calculated by averaging the data daily, over one year, for all the years. The correlation coefficient was found to be greater than 0.8. The original function was then corrected by removing the seasonal component by the following operation: r = a – b * M.S.D. ( a )/ M.S.D. ( b ) * c.c. where a is the original function, b the corrected function, M.S.D. is the mean square deviation and c.c. the correlation coefficient. In the corrected function the same anomalies calculated for the original function have been found. This approach, however, is not applicable to all types of data: in particular, neither in the lack of a seasonal variation nor in case of series shorter than one year. The following step was to use other multiparametric statistical tools, with specific attention to the Principal Component Analysis (PCA), in order to remove the meteorological effects by the time series of each site and to get series which consist of geodynamic interest information. The correct series have been, then, correlated to each other, and the joint analysis with the other observables included in the project DPC-INGV S3 is in progress. Results are expected to allow the formulation of large scale models and to give information about the investigating scale in space and time. To this purpose, the program ORIGIN 9.0 (Microcal Software, 2009) was used, which allowed to apply the PCA methodology (Joliffe, 2002; Shlens, 2009; Bailey, 2012) to the radon series and to the time series of temperature and pressure. Only those periods were considered in which the temperature and pressure values were present along with the radon value.