Abstract. In this paper we use the Cross Correlation analysis method in conjunction with the Empirical Mode Decomposition to analyze foF2 signals collected from Rome, Athens and San Vito ionospheric stations, in order to verify the existence of seismo-ionospheric precursors prior to M=6.3 L'Aquila earthquake in Italy. The adaptive nature of EMD allows for removing the geophysical noise from the foF2 signals, and then to calculate the correlation coefficient between them. According to the cross correlation coefficient theory, we expect the stations which located inside the earthquake preparation area, as evaluated using Dobrovolsky equation, to capture the ionospheric disturbances generated by the seismic event. On the other hand the stations outside of this area are expected to remain unaffected. The results of our study are in accordance with the theoretical model, evidencing ionospheric modification prior to L'Aquila earthquake in a certain area around the epicenter. However, it was found that the selection of stations at the limits of the theoretically estimated earthquake preparation area is not the best choice when the cross correlation method is applied, since the modification of the ionosphere over these stations may not be enough for the ionospheric precursors to appear. Our experimental results also show that when a seismic event constitutes the main shock after a series of pre-seismic activity, precursors may appear as early as 22 days prior to the event.

Abstract. Ionospheric variability as a result of earthquake events is a confirmed phenomenon as published in various seismo-ionospheric coupling studies. Generally, ionospheric variations resulting from earthquake activity are much weaker than disturbances generated by different sources, e.g. geomagnetic storms. However, geomagnetic storm disturbances exhibit more global behaviour, whereas seismo-ionospheric variations occur only locally in an area that is specified by the magnitude of the earthquake. Crosscorrelation coefficient analysis is a technique proposed some years ago, and ensures cancelation of geomagnetic storm variations of the ionospheric plasma, provided that the measurements are taken from stations with similar behaviour in these phenomena. In this paper we will use the aforementioned technique for analyzing data from ionospheric stations in Rome and Athens, and apply it to a series of earthquakes in Greece. Considering the local behaviour of the seismoionospheric variations, we expect that the Athens station, which happens to be inside the area affected by the earthquake, will accurately capture the disturbances. Due to its distance from the activity, we also do not expect the Rome station measurements to be affected by the seismic events in Greece. In addition, due to the fact that ionospheric plasma parameters exhibit non-stationary and nonlinear behaviour, we propose a novel signal processing technique known as the Hilbert-Huang transform in order to denoise the data before we calculate the cross-correlation coefficient of the two signals. Results from our analysis are in accordance with previously-conducted studies covering the same topic, clearly demonstrating that there are ionospheric precursors 1 to 7 days prior to strong seismic events as well as 1 to 2 days following such events.

The notion of fuzzy entropy (FuzzyEn) is extended to the multiscale case by combining FuzzyEn and empirical mode decomposition (EMD). The proposed technique, fuzzy intrinsic entropy (FIMEn) performs better than its predecessor intrinsic monde entropy (IMEn) and it is less dependent on the algorithmic parameters. In a pattern recognition context, FIMEn provides more separable clusters than IMEn when used for feature extraction, thus allowing for less classification error. The above results suggest that the proposed multiscale entropy metric is a very promising technique for evaluating data regularity and can be used effectively for feature extraction in pattern recognition problems.