Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, Tito Scalo (PZ), Italy

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.

Through this paper the robust approach to monitoring volcanic aerosols by satellite is applied to an extended set of events affecting Stromboli and Etna volcanoes to assess its performance in automated detection of eruptive clouds and in monitoring pre-eruptive emission activities. Using only NOAA/AVHRR data at hand (without any specific atmospheric model or ancillary ground-based measurements) the proposed method automatically discriminates meteorological from eruptive volcanic clouds and, in several cases, identified pre-eruptive anomalies in the emission rates not identified by traditional methods. The main merit of this approach is its effectiveness in recognising field anomalies also in the presence of a highly variable surface background as well as its intrinsic exportability not only on different geographic areas but also on different satellite instrumental packages. In particular, the possibility to extend the proposed method to the incoming new MSG/SEVIRI satellite package (which is going to fly next year) with its improved spectral (specific bands for SO 2 ) and temporal (up to 15 min) resolutions has been evaluated representing the natural continuation of this work.

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.

We analyse geoelectrical and geochemical time series jointly measured by means of a multiparametric automatic station close to an anomalous fluid emission in Val d'Agri (Basilicata, Southern Italy). In the investigated are some destructive seismic events occurred in past and recent years. We analysed the temporal fluctuations of the signals by spectral tools. We detected scaling behaviours in the power spectra of the time series recorded, that are typical fingerprints of fractional Brownian motions. The estimated values of the spectral indices reveal the presence of antipersistent behaviour in the time dynamics of all geoelectrical and geochemical data recorded. This work intends to improve our knowledge of the inner time dynamics of geophysical non-seismometric parameters.

Nakamura’s technique, or the H/V spectral ratio method, has been applied to microtremor measurements carried out in the urban area of Catania (Eastern Sicily) to obtain information on the geological structure of some sites,and to make a hypothesis on their seismic response. In general, sites located on soft soils or anthropic debris fillings have shown greater amplification at high frequencies (above 1 Hz). However, a strong lateral variation was observed in the frequency band, thus a denser grid of measurement points is necessary for a precise mapping of the resonant frequencies. In the low frequency range, between 0.1 and 1 Hz, a common peak around 0.2 Hz was observed. The fundamental resonant frequency inferred from the main peak in the H/V spectrum has been used to calculate the depth of the interface between the clays and the main reflector on the basis of the shear-wave velocity: it has been estimated as about 700 m.

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.