## 5 Discussion  
In this paper, precursor anomalies possibly associated with the 2022 M\({}_{\rm L}\) = 5.7 Fano EQ (Marche, Italy) were studied using a multiparametric and multilayer approach, including seismic, atmospheric, and ionospheric parameters. The purpose of this multidisciplinary approach is to gather various contributions and connect them to identify the best LAIC model.  
Although the resolutions of data are different, we are confident that, when we assimilate them at daily intervals (see Table 2), we can reconstruct a reliable cumulative number of anomalies. The tracking of the cumulative number of anomalies in chronological order reveals a distinctive behavior, as illustrated in Figure 16.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Date** & **Days to Mainshock** \\ \hline R-AMR & 11 September 2019 & \(-\)1155 \\ R-AMR & 11 August 2021 & \(-\)455 \\ SKT & 18 August 2022 & \(-\)82 \\
**Swarm A mag. field** & **26 August 2022** & \(-\)**75** \\ OLR & 05 September 2022 & \(-\)65 \\ OLR & 12 September 2022 & \(-\)58 \\ SKT & 15 September 2022 & \(-\)54 \\ Ionosonde & 31 October 2022 & \(-\)9 \\ Electron bursts & 08 November 2022 & \(-\)1 \\ Swarm A mag. field & 13 November 2022 & \(+\)4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of anomalies detected for the case study of the Fano EQ. From right to left, it shows the analyzed parameter, the day when the anomaly was identified, and the days of occurrence relative to the mainshock. All anomalies appear from bottom (lithosphere) to top (atmosphere and ionosphere). There is only one exception (indicated in bold): a satellite anomaly appears among the atmospheric anomalies.  
Figure 15: Three-dimensional representation of the NOAA-15 semi-orbits on 8 November 2022; EB evidenced by a red circle while the star identified the EQ epicenter.  
An exponential fit (indicated by the red curve) accurately represents the overall acceleration of anomalies. This collective progression of anomalies from different geolayers cannot simply be attributed to chance and is probably associated with the preparation phase of the Fano EQ. Furthermore, as highlighted in Table 2, most anomalies appear chronologically from the lithosphere to the atmosphere and ionosphere. This pattern suggests these can be defined as \"thermodynamic anomalies\", related to a diffusive-delayed coupling model likely driven by thermodynamic processes. Notably, there is an ionospheric satellite anomaly (indicated in bold in Table 2) amidst the atmospheric anomalies, which could be due to direct electromagnetic coupling between the lithosphere and the ionosphere.  
Based on the multiparametric and multilayer analysis, a long-term precursor in the lithosphere was identified. For example, the R-AMR value showed its anomalous acceleration starting about three years before the EQ. From the analysis of atmospheric and ionospheric parameters, anomalies were detected starting 82 days before the Fano EQ. Specifically, the energetic particle signal from NOAA showed an anomaly one day before the seismic event. The Swarm satellites detected an anomaly 75 days before the event and another 4 days after, likely associated with aftershocks. An anomaly was recorded in the ionosonde 9 days before the EQ, and atmospheric anomalies were mainly detected at \(-82\) days and \(-54\) days. The number of anomalies in the atmosphere and ionosphere for this EQ is comparable. This similarity suggests a LAIC behavior of the \"thermodynamic\" or \"diffusive-delayed\" coupling, with progression from the lithosphere through the atmosphere to the ionosphere. This progression might correspond to the chemical channel or the acoustic gravity channel, as described in the Hayakawa model [1].