Lapenna, V.

The Normalized Wavelet Cross-Correlation Function (NWCCF) was used to study correlations between the series of extreme events in self-potential data and earthquakes, both modelled as stochastic point processes. This method gives objective results, robust to the presence of nonstationarities that often affect observational time series. Furthermore, the NWCCF identifies the timescales involved in the cross-correlated behaviour between two point processes. In particular, we analyzed the cross-correlation between the sequence of extreme events in selfpotential data measured at the monitoring station Tito, located in a seismic area of Southern Italy, and the series of earthquakes which occurred in the same area during 2001. To evaluate the influence of rain on the dynamics of geoelectrical variations, we applied the same approach between the selected extreme values and the rain data. We find that the anomalous geoelectrical values seem to cross-correlate with the rain at short and intermediate timescales (t< 500 h), while they significantly cross-correlate only with earthquakes (M = 2.5) at long timescales (t> 500 h).

n this work we present the main features of a new multiparametric station able to jointly detect self-potential and seismometric signals in a seismic active area of Southern Italy. The new station has been designed and installed at the Tito Laboratories of National Research Council (Italy) that are located in the Southern Apennines, one of the most tectonically active areas of the whole Mediterranean. It combines advanced technologies for data acquisition with robust statistical techniques to pick out extreme events from self-potential recordings. The completely automatic station is equipped with electrical and seismometric sensors (16 channels, A/D 24 bit, sampling rate of 0.25 Hz, range dynamics of 133 dB). After a preliminary filtering procedure, mainly devoted to removing any influence of meteo-climatic conditions and/or cultural electrical noise, we evaluated the performance of the new monitoring station investigating the possible correlation between anomalous patterns of the self-potential signals and local seismic activity. Objective criteria and robust statistical tools have been applied to identify extreme events in electrical measurements and to select the earthquakes that may be responsible for strain effects at the measuring point. The short period of the measuring activity does not allow us to give firm conclusions, however the first results encourage us to continue the monitoring activity by increasing the number of remote stations and improving the use of statistical packages for data processing. We identified a well based monitoring strategy that in the near future could be useful to better understand the possible correlation between anomalous self-potential signals and local seismic activity.