Lapenna, V.

Self-potential time series are investigated to characterise self-potential time scales. The data analysed were recorded in stations located in two active seismic areas of the Mediterranean region, the Southern Apennine chain (Giuliano) and Crete Island (Heraklion), where in past and recent years many destructive seismic events have taken place. The seismological and geological settings, combined with a low level of cultural noise, allow us to consider these areas ideal outdoor laboratories to study the time dynamics of geophysical parameters of electrical nature. At the same time, the different seismological features of such areas make an inter-comparison between the geoelectrical variability observed at the two sites interesting. Fractal analysis tools, able to detect scale laws and quantify persistence features, are used to better understand the background variability properties of the self-potential signals. As results from our analysis, antipersistence seems to be a ubiquitous feature on short time scales (minutes) regardless of environmental conditions. On such scales, the accumulation of random fluctuations is not particularly efficient and significant variations mostly occur as sudden jumps.

Time scale properties of self-potential signals are investigated through the analysis of the second order structure function (variogram), a powerful tool to investigate the spatial and temporal variability of observational data. In this work we analyse two sequences of self-potential values measured by means of a geophysical monitoring array located in a seismically active area of Southern Italy. The range of scales investigated goes from a few minutes to several days. It is shown that signal fluctuations are characterised by two time scale ranges in which self-potential variability appears to follow slightly different dynamical behaviours. Results point to the presence of fractal, non stationary features expressing a long term correlation with scaling coefficients which are the clue of stabilising mechanisms. In the scale ranges in which the series show scale invariant behaviour, self-potentials evolve like fractional Brownian motions with anticorrelated increments typical of processes regulated by negative feedback mechanisms (antipersistence). On scales below about 6 h the strength of such an antipersistence appears to be slightly greater than that observed on larger time scales where the fluctuations are less efficiently stabilised.

In this paper we explore the time dynamics of geoelectrical signals measured in two seismic active areas in the Mediterranean region: the Southern Apennine chain (Italy) and the Hellenic arc (Crete Island, Greece). After apreliminary filtering procedure carried out to remove man-made and climatic noises, the geoelectrical time seriesmeasured in both the seismological environments show features that are typical fingerprints of stochastic processes.In particular the time fluctuations follow a dynamics well described by an autoregressive model of a first order(red noise). The model has been tested in the frequency and time domains applying advanced statistical methodologies. Taking into account these results, we propose an objective methodology to pick out from geoelectrical time series anomalous patterns from background noise and we study the possible correlation between the appearance of extreme events in the electrical signals and the local seismic activity. Finally an in dept analysis of results obtained in the two investigated areas has been performed.

The time dynamics of geoelectrical precursory time series has been investigated and a method to discriminate chaotic behaviour in geoelectrical precursory time series is proposed. It allows us to detect low-dimensional chaos when the only information about the time series comes from the time series themselves. The short-term predictability of these time series is evaluated using two possible forecasting approaches: global autoregressive approximation and local autoregressive approximation. The first views the data as a realization of a linear stochastic process, whereas the second considers the data points as a realization of a deterministic process, supposedly non-linear. The comparison of the predictive skill of the two techniques is a test to discriminate between low-dimensional chaos and random dynamics. The analyzed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy) in one of the most seismic areas of the Mediterranean region. Our findings are that the global (linear) approach is superior to the local one and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. Power spectra of the filtered time series follow a P(f) = F-a scaling law: they exhibit the typical behaviour of a broad class of fractal stochastic processes and they are a signature of the self-organized systems.

An autoregressive model was selected to describe geoelectrical time series. An objective technique was subsequently applied to analyze and discriminate values above (below) an a priorifixed threshold possibly related to seismic events. A complete check of the model and the main guidelines to estimate the occurrence probability of extreme events are reported. A first application of the proposed technique is discussed through the analysis of the experimental data recorded by an automatic station located in Tito, a small town on the Apennine chain in Southern Italy. This region was hit by the November 1980 Irpinia-Basilicata earthquake and it is one of most active areas of the Mediterranean region. After a preliminary filtering procedure to reduce the influence of external parameters (i.e. the meteo-climatic effects), it was demonstrated that the geoelectrical residual time series are well described by means of a second order autoregressive model. Our findings outline a statistical methodology to evaluate the efficiency of electrical seismic precursors.