## 3 Results
The EQ magnitudes, which are read in the right scale, are denoted by the (black) vertical lines ending at solid circles. The horizontal magenta and cyan lines correspond to the values 0.285 (=\(\beta_{0}\)) and 0.5, respectively. The thick dark brown line resembling a dichotomous (ON/OFF) signal, indicates when an alarm is ON by taking the value 1. Otherwise, there is no alarm. The average DFA exponent \(\langle\alpha\rangle\) discussed in Section 4 is also shown by the green lines. The analysis of \(\beta_{100}\) and \(\beta_{160}\) leads to the 12 'precursory' fluctuation minima shown in Table 1. In this Table, we also insert the two DFA exponents \(\alpha_{160}\) and \(\alpha_{300}\), which were calculated from the EQ magnitude time series on the date of \(\beta_{160,min}\) when considering either the preceding 160 or 300 EQs, respectively. They lead to a better classification of the 'precursory' fluctuation minima as it will be discussed in the next Section. These 'precursory' fluctuation minima were followed within 9 months by the 13 strong EQs labeled by H1 to H6 and FA1 to FA6b in Table 2.  
In accordance with References [79; 81], we used the date of \(\beta_{160,min}\) as the last date of the EQ catalog and constructed the \(\langle\)EPS\(\rangle\) maps for \(R=200\) km. The latter are shown in Figures 5 and 6. In these two Figures, the epicenters of the strong EQs that took place (within 9 months) after the 'precursory' fluctuation minimum (see Tables 1 and 2) are also inserted. In Figure 6g,h, the \(\langle\)EPS\(\rangle\) maps were constructed on the dates 24 April 2017 and 6 July 2018, respectively, when non-'precursory', i.e., not satisfying Conditions (10) and (11), fluctuation minima were observed, see Figure 4, but were also followed by the two strong \(M=8.2\) EQs labeled C1 and C2 in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**EQ Name** & **M** & **Lon. (\({}^{o}\)E)** & **Lat. (\({}^{o}\)N)** & **EQ Date** & **Label** \\ \hline
**Sumatra-Andaman** & 9.0 & 95.78 & 3.30 & 26 December & \\
**Sumatra-Nias** & 8.6 & 97.11 & 2.09 & 28 March & H2 \\
**Sumatra-Indonesia** & 8.5 & 101.37 & \(-\)4.44 & 12 September & H3 \\
**Papua-Indonesia** & 7.7 & 132.88 & \(-\)0.41 & 3 January & FA1 \\
**Chile** & 8.8 & \(-\)72.71 & \(-\)35.85 & 27 February & \\  & & & & 2010 & H4 \\
**Tohoku-Japan** & 9.1 & 142.37 & 38.32 & 11 March & H5 \\
**Indian Ocean** & 8.6 & 93.06 & 2.33 & 11 April 2012 & H6 \\ Solomon Islands & 7.9 & 165.11 & \(-\)10.80 & 6 February & FA2 \\ Iquique-Chile & 8.1 & \(-\)70.77 & \(-\)19.61 & 1 April 2014 & FA3 \\ Illapel-Chile & 8.3 & \(-\)71.67 & \(-\)31.57 & 16 September & FA4 \\
**Chiapas** & 8.2 & \(-\)93.90 & 15.02 & 8 September & C1 \\  & & & & 2017 & C1 \\ Fiji & 8.2 & \(-\)178.15 & \(-\)18.11 & 19 August & C2 \\ Alaska & 7.8 & \(-\)158.55 & 55.07 & 22 July 2020 & FA5 \\ Chignik & 8.2 & \(-\)157.89 & 55.39 & 29 July 2021 & FA6a \\ Sandwich & 8.3 & \(-\)25.19 & \(-\)57.60 & 12 August & FA6b \\  & & & & 2021 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the 15 strong EQs discussed in the text. In each case, in addition to the EQ magnitude, epicenter location, and date, a label is also ascribed by relating the EQ to \(\beta_{W}\) minima. These labels have the following meaning: H1 to H6 correspond to true positive precursory variability minima, FA1 to FA6 to false positive variability minima (FA6a and FA6b are related to the same minimum FA6 of Table 1), while cases C1 and C2 correspond to the minima of \(\beta_{160}\) observed on 24 April 2017 and 6 July 2018, respectively. For the details of the precursory variability minima see Table 1. The EQ names for EQs with \(M\geq 8.5\) are typed boldface. Figure 5: Average EPS maps calculated on the date that \(\beta_{160}\) minimizes (see Table 1) for the case of coarse grain radius \(R=200\) km. Panels (**a–f**) correspond to the \(M\geq 8.5\) EQs Sumatra–Andaman, Sumatra–Nias, Sumatra–Indonesia, Chile, Tohoku–Japan, and Indian Ocean, respectively (see also Table 2).