## 3 Results  
The analysis of our recorded \({}^{220}\)Rn time series in soils within the Campi Flegrei caldera area was performed using the sequential application of separate signal processing methods (together forming a hybrid method), described in Section 2.2, focused on the recognition of the trends and the possible residuals (anomalies) of the signals [36, 45, 46, 47]. The daily time series of \({}^{220}\)Rn activity concentration in soils, over the period 1 July 2011-31 December 2017, are displayed in Figure 2a,b for OLB and MSA sites, respectively. The same figure also reports the \({}^{222}\)Rn (hereafter also radon) activity concentration time series, taken from [1], with the aim to highlight the differences in the recorded signals of the two isotopes of the noble gas. All four signals were corrected for the recorded influence of outside driving forces, i.e., temperature, relative humidity and pressure, through the Multiple Linear Regression (MLR) technique [37]. This means that the influences of those driving forces were filtered out from radon and thoron signals. The occasional lack of data in the time series was treated with the Self-Consistent Regression Estimator (SCRE) technique that recursively imputes missing values [40]. As carried out in [1] for the \({}^{222}\)Rn time series, here the \({}^{220}\)Rn time series was investigated with the Empirical Mode Decomposition (EMD) for distinguishing and extracting the different components and trends [46]. Most of the components obtained by applying the EMD method (not showed for brevity) are due to meteorological and seasonal phenomena. The trend is the last component obtained by EMD, which represents the smooth component containing information about time series global change [24]. The residuals (i.e., anomalies) refer to those values not attributable to the normal evolution of the studied time series [45]; here, they were recognized as the values of the \({}^{220}\)Rn time series out of the 95% confidence interval (95% CI) of the differences between the measured signals and the forecasted time series [1, 43]. The forecasted time series was computed by the application of the forecasting Support Vector Regression (SVR) method on the sum of the previous components obtained from EMD method [42]. The residuals (anomalies) and the trends of \({}^{220}\)Rn time series at OLB and MSA are shown in Figure 3a,b, respectively, together with the same ones obtained for \({}^{222}\)Rn from [1]. The trends (Figure 3b) underline an increasing pattern of \({}^{220}\)Rn activity concentration during the entire monitoring period at both OLB and MSA sites with different intensity, as well as in the \({}^{222}\)Rn trends. Instead, thoron residuals concentrate in four well-defined periods (March-November 2013, May-September 2014, April-November 2015 and August 2016-December 2017; Figure 3a). The \({}^{222}\)Rn residuals concentrate in about the same four periods. It is interesting to note how the \({}^{222-220}\)Rn residuals occur almost simultaneously at both sites, even if with different intensity. The different intensity of the residuals and trends of \({}^{222}\)Rn, and here also manifested in the \({}^{220}\)Rn, lower in MSA than in OLB, was already explained in [1], as a consequence of the different distance of \({}^{222-220}\)Rn stations from the center of the current unrest that roughly coincides with the central area of the Campi Flegrei caldera (\(\sim\)1 km for OLB and \(\sim\)4 km for MSA, Figure 1), where the hydrothermal activity is concentrated and where the maximum ground uplift and the highest seismicity are recorded. The same explanation can therefore be assumed for the thoron results. In addition, comparing the radon-thoron results in Figure 3, a new consideration emerges from the present study; the intensity of thoron trends are almost halved, while the residuals are three times less, with respect to those of radon, in both monitoring sites.