# Successful Tests on Detecting Pre-Earthquake Magnetic Field Signals from Space
## 3 Results and Discussion
### Extended Validation Analyses as the Base for an OEPS  
In this section, a more comprehensive analysis is conducted to depict the balance between success rate and false alarm rate [52; 53; 54; 55]. For this reason, the results have been statistically analyzed based on the confusion matrix and relevant statistical metrics that have been measured [53]. In this analysis, we consider the number of orbits examined in each region on a daily basis. For a more rapid and realistic analysis, we did not consider geomagnetic indices (they need some time to be estimated from the World Centers, so they would not be quickly available for real-time analysis). Specifically, if an orbit shows an anomaly and an earthquake occurs within the range of that anomaly within the next 10 days, it is classified as a true positive. If no earthquake occurs, it is classified as a false positive. If no orbits indicate any anomalies, then a radius of 300 km will be considered as the effective earthquake distance and searched for the next 10 days. The choice of 300 km is an optimal conservative compromise with respect to a normally larger Dobrovolsky region (which happens when M \(\geq\) 5.8). If no earthquake occurs within this radius, it will be classified as a true negative. If an earthquake does occur, it will be classified as a false negative. The elements of the confusion matrix are completed according to Figure 4, and, on the basis of Table 5, we evaluate the following statistical quantities (e.g., [53]): accuracy, A = 79%; precision, P = 88%; hit rate, H= 84%; F1-score, F1 = 86%; and false alarm rate, F = 36%. As evident, the confusion matrix demonstrates very high accuracy, precision, F1-score, and hit rate. These results are outstanding: they significantly surpass those obtained by recent works (e.g., [52; 53]). However, there is a false alarm rate of 36%, which is not negligible, so the results found for real-time earthquake prediction should be used with great care. In that case, we would suggest some further actions to improve the OEPS and reduce the false alarm rate as much as possible. The first two actions will be performed in the satellite anomaly identification. First, we can avoid moments with significant disturbed geomagnetic activity. This will exclude potential false anomalies, due to the solar wind and magnetosphere-ionosphere interaction instead of the preparatory phase of earthquakes. Secondly, we can check for repetition of anomalies in subsequent orbits: only those showing some minimal repetition will be taken. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multicolumn{2}{c}{**Number of EQs That**} & \multirow{2}{*}{**Total Number of EQs**} & \multirow{2}{*}{**Percentage (\%)**} \\  & & & \\  & & & \\ \hline Base & 615 & 786 & 78 \\ \hline Shift & 132 & 770 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of earthquakes in the base and shift regions considering the geomagnetic conditions that had anomalies in both the baseline and shifted regions, considering quiet geomagnetic conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multicolumn{2}{c}{**Number of EQs That**} & \multirow{2}{*}{**Total Number of EQs**} & \multirow{2}{*}{**Percentage (\%)**} \\  & & & \\  & & & \\ \hline Base & 900 & 1076 & 83 \\ \hline Shift & 256 & 1069 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of earthquakes in the base and shift regions regardless of geomagnetic conditions, and the percentage of earthquakes that had anomalies in both the baseline and shifted regions. The other two actions can be undertaken after the anomaly identification. Thirdly, we look for consistency in the estimated magnitude among the different equations deduced from duration or energy, say within 0.5 magnitude units.