## 4 Discussion
### Improving the Superposed Epoch Approach by Taking into Account the Rikitake Law
Figure 7 shows the results for the _Swarm_ data without taking into account the frequency of the signal. The maximum concentration has a higher statistical significance with respect to the previous analyses: in fact, for the magnetic field data, \(d\) passes from 1.7 to 2.1, and for the _Ne_ data, \(d\) passes from 1.3 to 1.5, which also means that, from a non-statistically  
Figure 6: Expected anticipation time of magnetic field (**A**) and _Ne_ (**B**) anomalies with respect to the earthquake, using the Rikitake law [37] and considering the coefficients found by [PERSON] et al. [29] for 4.7 years of _Swarm_ data (orange line). The investigated time window is shown as a blue line, and the 8-year _Swarm_ dataset maximum size is shown as a dashed red line. The data tips provide the time that has been analyzed before a specific magnitude earthquake, applying Equation (4). significant result, we obtain a higher significance with this different superposed time approach, which implies that we could better investigate the identified anomalies to search for seismo-ionospheric disturbances. In the same way, in Figure 8, we apply this approach to the anomalies extracted by analyzing the frequency content of the magnetic field and _Ne_ data. The frequency selected is the best from the previous analysis, so we obtained the magnetic field data in the quicker signal (2-10 s) and the _Ne_ data in the slower signals (25-50 s). In this case, there is a general gain in the statistical improvements of both the \(d\) and \(n\) parameters (for the magnetic field data: \(d\) passes from 1.8 to 2.0, and \(n\) passes from 9.5 to 11.4; for the _Ne_ data, \(d\) passes from 2.5 to 4.0, and \(n\) passes from 27.2 to 98.3, recording a very strong increase in statistical significance). Finally, we underline that this approach seems very promising but requires a very large dataset. In fact, for the larger magnitude earthquakes, the whole time is not covered, even with 8 years of satellite data. On the other hand, for the visual inspection of the results, the immediate reading is lost with this approach: for example, the magnetic field data of  
Figure 8: _Savarm_ magnetic field data in the shorter period of 2–10 s (Y-mag in (**A**)) and electron density in the longer period of 25–50 s (_Ne_ in (**B**)) correlated with M5.5+ earthquakes using modified time according to the empirical Rikitake law. Figure 7: _Savarm_ magnetic field data (Y-mag in (**A**)) and electron density (_Ne_ in (**B**)) correlated with M5.5+ earthquakes using modified time according to the empirical Rikitake law. Figure 7A show several concentrations of anomalies between 75 days and 1 month before the M6.5 earthquakes, but such time corresponds to a different interval for any earthquake magnitude that can be estimated by Equation (4) and is represented in Figure 6A. However, as the empirical Rikitake law seems to describe the possible precursor well, it is fundamental to consider it and include it in a future prediction platform. In practice, detecting an anomaly can indicate a lower magnitude earthquake or a larger magnitude earthquake at a later time, and, if a lower magnitude earthquake does not happen immediately, after a number of days/weeks, the prediction can be adjusted, increasing the magnitude of the expected earthquake. Unfortunately, the larger number of false alarms (the anomalies not associated with any seismic events) makes it still impossible to implement such a prediction system.