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This paper deals with a geophysical survey carried out in some critical urban areas of the historical city of Matera (Southern Italy). Matera has a very complex shallower stratigraphy characterized by both anthropic and natural “targets” and is affected by geological instability. Therefore, Matera represents an ideal and very challenging outdoor laboratory for testing novel approaches for near-surface explorations in urban areas. Here, we present the results of a near-surface survey carried out by jointly applying Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) methods. The survey was implemented in three different critical zones within the urban area of Matera (Piazza Duomo, Piazza San Giovanni, Villa dell’Unità d’Italia). These test sites are of great interest for archaeological and architectonical studies and are affected by ground instability phenomena due to the presence of voids, cavities and other anthropic structures. The effectiveness of the survey was enhanced by the exploitation of advanced 3D tomographic approaches, which allowed to achieve 3D representation of the investigated underground and obtain information in terms of both the location and the geometry of buried objects and structures and the characterization of shallow geological layers. The results of the surveys are now under study (or have attracted the interest) of the Municipality in order to support smart cities programs and activities for a better management of the underground space.

The scaling behaviour of the 1981–2007 seismicity data in central Italy, which is one of the most seismically active areas in Italy is investigated. In particular we examined the earthquakes located in a circular area centred on the epicentre of the strongest event, occurred in September 26, 1997 (duration magnitude M D = 5.8 ). On the base of the detrended fluctuation analysis (DFA), we found that in the magnitude range between 2.5 and 2.9 the scaling exponents fall into disjoint sets for events relatively close and far from the epicentre of the strongest event.

We investigated the dynamics of particulate matter data, recorded in Tito, a small industrial area of southern Italy. The analysis of these signals was performed using the Fisher information measure (FIM), which is a powerful tool for investigating complex and nonstationary signals, and the Shannon entropy, which is a well-known tool for investigating the degree of disorder in dynamical systems. Our results point to an increase of disorder and complexity from fine to coarse particulates.

The joint application of electromagnetic techniques for near-surface exploration is a useful tool for soil pollution monitoring and can also contribute towards describing the spatial distribution of pollutants. The results of a geophysical field survey that was carried out for characterizing the heavy metal and waste disposal soil pollution phenomena in the industrial area of Val Basento (Basilicata region, Southern Italy) are presented here. First, topsoil magnetic susceptibility measurements have been carried out for defining the spatial distribution of superficial pollution phenomena in the investigated area. Second, detailed and integrated measurements based on a high-resolution magnetic mapping and ground probing radar (GPR) profiling have been applied to investigate the subsurface in two industrial areas located in more polluted sites that were identified during the first phase. Our monitoring strategy discloses the way to rapidly define the zone characterized by high pollution levels deriving from chemical industries and traffic emissions and to obtain the way information about the presence of local buried sources of contamination.

We investigate, by the Detrended Fluctuation Analysis (DFA), the long-range correlations in the hourly time variability of geoelectrical signals measured by the station Tramutola, located in one of the most seismic area of southern Italy. The DFA power-law exponent, characterizing the temporal fluctuations of these signals, shows significant variation, possibly related with the seismic activity of the area investigated.

The time and space anomalous behaviour of the Self-Potential (SP) field recorded in a seismic area of Southern Apennines, Italy, is discussed. The SP data were collected in the period June 1992-November 1994 along a profile located north of the town of Potenza in the Basilicata region, Italy. The profile is perpendicular to an active fault system, where a W-E directed strike-slip structure has been identified from recent earthquakes. The SP data are modelled using a new tomographic method based on the search for similarities between the observed SP sequence and the surface signature of the electric field due to a scanning point source with unitary positive charge. The point scanner is ideally moved in a vertical cross-section through the profile and a regular 2D matrix of charge occurrence probability values is thus obtained. These values are used to image the state of electric polarization in the subsoil, compatible with the observed SP surface pattern. A selection of 2D tomographies across the profile is then discussed in order to outline the SP source geometry and dynamics within the faulted structure. Finally, the time pattern of the SP polarization state is compared with the local seismicity in the frame of the rock dilatancy-fluid diffusion theory. This comparison allows us to exclude a direct relationship of the SP time behaviour with the seismic sequences which occurred in the area during the SP monitoring period.

We present the results regarding a combined analysis of self-potential and acoustic emission time series, jointly monitored during 1996 by means of a remote station installed north of the town of Potenza, close to an active fault system in a seismic area of Southern Italy. The goal is to verify the existence of correlations between geoelectric and seismoacoustic signals and the local seismicity. Preliminary filtering procedures for the removal of meteoclimatic effects and noise fluctuations of anthropic origin were applied. Then, objective methods were used to discriminate anomalous patterns from background noise in electric and seismoacoustic time series. Finally, a deep analysis of the possible correlations between the self-potential and acoustic emission extreme events and local seismicity has been carried out. The extreme events occurred before the Md = 4.5 earthquake of April 3, 1996 seem to indicate a common physical mechanism likely referable to the dilatancy-diffusion-polarization model.

A statistical method to evaluate extreme events in earthquake precursory signals of electrical nature is proposed. The occurrence probability of seismo-electrical anomalies is computed by means of a parametric time series approach. In order to avoid the influence of external noise, a preliminary filtering of the electrical signals is performed. In a second step an autoregressive model to describe the residual time series is suitably identified and fitted to the data. Finally, parametric inference of extreme events is carried out on the basis of the selected model. The performance of the technique has been evaluated by considering geoelectrical time series recorded by an automatic station, located on the Southern Apennine chain (Italy). This region was interested by the November 1980, Irpinia-Basilicata earthquake and it is one of the most active areas of the Mediterranean region. Finally, a possible correlation between electrical anomalies and seismic events occurred in the area is discussed.

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.