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The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present remarkable MFD variability during the complex 2016/17 central Italy sequence using a high-resolution earthquake catalog. Isolating seismically active volumes (‘clusters’) reveals that the MFD differed in nearby clusters, varied or remained constant in time depending on the cluster, and increased in b-value in the cluster where the largest earthquake eventually occurred. These findings suggest that the fault system’s heterogeneity and complexity influence the MFD. Our findings raise the question “b-value of what?”: interpreting and using MFD variability needs a spatiotemporal scale that is physically meaningful, like the one proposed here.

Most students living in disadvantaged areas do not trust that their school education will have much bearing on their future and become disaffected from school as early as in lower secondary school.We here report the results of teaching Geosciences through several practical activities carried out in suburban schools characterized by a high dropout rate. The lectures were given in the framework of the STEM project Next Land, which has the overarching goal of instilling interest towards scientific topics into young students and promoting the role of scientific education for sustainable development.We propose a sequence of four laboratory activities on the subject of natural calamities (e.g. earthquakes and tsunamis), which are based on the use of both manual and IT skills.The didactical experimentation has been conducted on 21 seventh-grade classes (age ~12) involving about 350 students from the area of Naples (Southern Italy). The final aim of the teaching is to test the potential of the Geosciences in generating attitudes and behaviours of solidarity and responsible global citizenship.All the proposed activities try to make students aware that they are part of a connected global system, therefore through these lectures we attempt to help these young students to put in perspective their local reality in a larger one.

The area of Naples, in southern Italy, is densely populated and seriously affected by a variety of environmental hazards related to meteorological conditions. The city is characterized by a complex topography therefore the meteorological risks sensibly vary throughout its territory. In the past decade, heat waves have been increasingly frequent: the production of heat maps would help the comprehension of the differential risks in town and would allow more accurate information on the related health risks to be available to the population. At present, maps of heat wave vulnerability show the spatial distribution of single factors, whereas vulnerability is governed by several competing or additive factors, so ambiguities about the identification of the most vulnerable areas currently remain. In the attempt to overcome this limit and identify areas more vulnerable to the effects of heat waves not only confined in Naples city centre, but also in suburban areas, we will apply unsupervised machine learning algorithms to integrate information from different factors and provide detailed heat wave vulnerability maps.

Leachate is the main source of pollution in landfills and its negative impacts continue for several years even after landfill closure. In recent years, geophysical methods are recognized as effective tools for providing an imaging of the leachate plume. However, they produce subsurface cross-sections in terms of individual physical quantities, leaving room for ambiguities on interpretation of geophysical models and uncertainties in the definition of contaminated zones. In this work, we propose a machine learning-based approach for mapping leachate contamination through an effective integration of geoelectrical tomographic data. We apply the proposed approach for the characterization of two urban landfills. For both cases, we perform a multivariate analysis on datasets consisting of electrical resistivity, chargeability and normalized chargeability (chargeability-to-resistivity ratio) data extracted from previously inverted model sections. By executing a K-Means cluster analysis, we find that the best partition of the two datasets contains ten and eleven classes, respectively. From such classes and also introducing a distance-based colour code, we get updated cross-sections and provide an easy and less ambiguous identification of the leachate accumulation zones. The latter turn out to be characterized by coordinate values of cluster centroids27 mV/V and 11 mS/m. Our findings, also supported by borehole data for one of the investigation sites, show that the combined use of geophysical imaging and unsupervised machine learning is promising and can yield new perspectives for the characterization of leachate distribution and pollution assessment in landfills.

SUMMARY With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences.

The detection and imaging of landfills is a challenging task for geophysical methods because major pitfalls may arise, in such complex areas, from the speculative interpretation of geophysical anomalies as geological or antrophic features. In fact, when we face a multi-layered scenario, with numerous resistive to conductive transitions (that is the case of landfills), the actual shape and position of the anomalies (e.g. due to leachate accumulation) can be biased. The use of electrical resistivity tomography (ERT) in combination with the induced-polarization (IP) method, can help in this sense, even though may be not sufficient to completely remove ambiguities in interpretation of inverted models.In this work, we present an application of an unsupervised machine learning k-means algorithm to ERT and IP data acquired in two urban waste disposal sites. The aim of the cluster analysis is to reduce the ambiguity on geophysical model interpretation and to improve the accuracy on detection of anomalous zones related to leachate accumulation. Experimental 2D field data were firstly inverted separately for resistivity and chargeability, using a Gauss-Newton algorithm. Then, joint 2D sections were obtained using k-means clustering of electrical resistivity, chargeability and normalized chargeability (chargeability divided by the resistivity) data. The retrieved model sections provide a quantitative integration of distinct geophysical data, which can offer new perspectives for the characterization of leachate distribution in landfills.

The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present unexpected MFD variability using a high-resolution earthquake catalog of the 2016–2017 central Italy sequence. Isolation of seismicity clusters reveals that the MFD differs in nearby clusters, varies or remains constant in time depending on the cluster, and features an unexpected b-value increase in the cluster where the largest event will occur. These findings suggest a strong influence of the heterogeneity and complexity of tectonic structures on the MFD. Our findings raise the question of the appropriate spatiotemporal scale for resolving the b-value, which poses a serious obstacle to interpreting and using the MFD in earthquake forecasting.

Complex interactions among organic contaminant, soil and water change the electrical properties of the subsurface often causing strong self-potential (SP) anomalies, whose monitoring is proposed as a useful tool to determine the temporal evolution of the contaminant plumes. In the present study, we focus on the problem of organic contaminant transport related to olive oil mill wastes (OOMWs), which represent an important environmental problem in Mediterranean countries. The diffusion of the contaminants into the subsurface is studied by using a global optimization procedure on SP data measured, at different times, in a well-studied contaminated pilot area located next to the Keritis river in western Crete island (Greece). Despite the complex hydrogeological conditions related to the proximity to both the Keritis river and a small seasonal stream, the analysis of five SP datasets acquired along the same profile shows that the proposed hybrid Genetic-Price algorithm is able to reproduce the main features of the SP signals, thus identifying probable multiple SP anomaly sources and their changes over time. In particular, preferential horizontal pathways of OOMW and their migration in the vadose zone during the summer season were identified, providing some useful insights for future planning of remediation actions.

Multiple self-potential (SP) anomalies are analyzed by using a Genetic-Price Algorithm (GPA), which has been recently introduced for the inversion of SP data. The proposed approach is tested on multiple synthetic anomalies, which are modeled by horizontal cylinders. First, a forward modeling is used to analyze the resolution of such anomalies by varying all model parameters. Then, GPA is applied to invert synthetic multiple SP anomalies. The numerical analyses show that the proposed approach is able to fully characterize the anomaly sources by providing the correct values of the model parameters as well as the number of sources, even if Gaussian random noise is added to the synthetic data. Furthermore, to show the computational efficiency of GPA, the results of a comparative analysis with the Very Fast Simulated Annealing algorithm are given. The validity of the GPA approach is confirmed by its application to three examples of self-potential field data from mineral exploration and groundwater investigations, which are presented and discussed in relation to other inversion approaches. Finally, the quantitative interpretation of multiple anomalies along a SP profile crossing the Mt. Somma-Vesuvius volcano caldera (southern Italy) is provided.

The medieval Basilica of Santa Maria in Compulteria was built at an important road junction in the Alife Plain (Campania region), a wide basin within the Apennine mountains of southern Italy. Archaeological excavations carried out during the 1990s at shallow depths under the floor of the Basilica discovered the remains of Roman Age buildings, but little is known about their extent and the older phases of occupation of this key site. We present here the results of two high-resolution 3D geophysical surveys – ground penetrating radar and electrical resistivity tomography – carried out to understand the nature and the extent of the structures buried down to about 10 m depth under the modern floor of the Basilica. The geophysical investigations allowed us to find evidence for older anthropogenic traces at a greater depth, which have never been hypothesized. An integrated interpretation of the geophysical measurements with previous archaeological data for the shallow part, and with admissible human traces at depth, has provided an exhaustive reconstruction of the subsurface remains from the upper to the deeper portion of the investigated volume. In particular, our findings highlight anomalous features of likely anthropogenic origin, which are ascribed to historical and pre-historical phases preceding the construction of the middle age Basilica.

Abstract An integrated approach that combines geophysical surveys and numerical simulations is proposed to study the processes that govern the fluid flow along active fault zones. It is based on the reconstruction of the architecture of the investigated fault system, as well as the identification of possible paths for fluid migration, according to the distribution of geophysical parameters retrieved by multi-methodological geophysical prospecting. The aim is to establish, thanks to constraints deriving from different types of data (e.g., geological, geochemical and/or hydrogeological data), an accurate 3D petrophysical model of the survey area to be used for simulating, by numerical modelling, the physical processes likely taking place in the imaged system and its temporal evolution. The effectiveness of the proposed approach is tested in an active fault zone in the Matese Mts (southern Italy), where recent, accurate geochemical measurements have registered very high anomalous values of non-volcanic natural emissions of CO2. In particular, a multi-methodological geophysical survey, consisting of electrical resistivity tomography, self-potential and passive seismic measurements, integrated with geological data, was chosen to define the 3D petrophysical model of the investigated system and to identify possible source geometries. Three different scenarios were assumed corresponding to three different CO2 source models. The one that hypothesizes a source located along the fault plane at the depth of the carbonate basement was found to be the best candidate to represent the test site. Indeed, the performed numerical simulations provide CO2 flow estimates comparable with the values observed in the investigated area. These findings are promising for gas hazards, as they suggest that numerical simulations of different CO2 degassing scenarios could forecast possible critical variations in the amount of CO2 emitted near the fault.

We present a structural study on late Miocene-early Pliocene out-of-sequence thrusts affecting the southern Apennine orogenic belt. The analyzed structures are exposed in the Campania region (southern Italy). Here, thrusts bound the N-NE side of the carbonate ridges that form the regional mountain backbone. In several outcrops, the Mesozoic carbonates are superposed onto the unconformable wedge-top basin deposits of the upper Miocene Castelvetere Group, providing constraints to the age of the activity of this thrusting event. Moreover, a 4-km-long N-S oriented electrical resistivity tomography profile, carried out along the Caserta mountains, sheds light on the structure of this thrust system in an area where it is not exposed. Further information was carried out from a tunnel excavation that allowed us to study some secondary fault splays. The kinematic analysis of out-of-sequence major and minor structures hosted both in the hanging wall (Apennine Platform carbonates) and footwall (Castelvetere Group deposits and Lagonegro-Molise Basin units) indicates the occurrence of two superposed shortening directions, about E-W and N-S, respectively. We associated these compressive structures to an out-of-sequence thrusting event defined by frontal thrusts verging to the east and lateral ramp thrusts verging to the north and south. We related the out-of-sequence thrusting episode to the positive inversion of inherited normal faults located in the Paleozoic basement. These envelopments thrust upward to crosscut the allochthonous wedge, including, in the western zone of the chain, the upper Miocene wedge-top basin deposits.

The coupled effects of climate change and land sinking make deltas and coastal areas prone to inundation and flooding, meaning that reliable estimation of land subsidence is becoming crucial. Commonly, land subsidence is monitored by accurate continuous and discrete measurements collected by terrestrial and space geodetic techniques, such as Global Navigation Satellite System (GNSS), Interferometry Synthetic Aperture Radar (InSAR), and high precision leveling. In particular, GNSS, which includes the Global Positioning System (GPS), provides geospatial positioning with global coverage, then used for deriving local displacements through time. These site-positioning time series usually exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. Although the periodic components observed in the geodetic signal affect the velocity estimate, studies dealing with the prediction and prevention of risks associated with subsidence focus mainly on the permanent component. Periodic components are simply removed from the original dataset by statistical analyses not based on the underlying physical mechanisms. Here, we propose a systematic approach for detecting the physical mechanisms that better explain the permanent and periodic components of subsidence observed in the geodetic time series. It consists of three steps involving a component recognition phase, based on statistical and spectral analyses of geodetic time series, a source selection phase, based on their comparison with data of different nature (e.g., geological, hydro-meteorological, hydrogeological records), and a source validation step, where the selected sources are validated through physically-based models. The application of the proposed procedure to the Codigoro area (Po River Delta, Northern Italy), historically affected by land subsidence, allowed for an accurate estimation of the subsidence rate over the period 2009–2017. Significant differences turn out in the retrieved subsidence velocities by using or not periodic trends obtained by physically based models.

In the last twenty years, a growing interest is noticed in quantifying non-volcanic degassing, which could represent a significant input of CO2 into the atmosphere. Large emissions of non-volcanic carbon dioxide usually take place in seismically active zones, where the existence of a positive spatial correlation between gas discharges and extensional tectonic regimes has been confirmed by seismic data. Extensional stress plays a key role in creating pathways for the rising of gases at micro- and macro-scales, increasing the rock permeability and connecting the deep crust to the earth surface. Geoelectrical investigations, which are very sensitive to permeability changes, provide accurate volumetric reconstructions of the physical properties of the rocks and, therefore, are fundamental not only for the definition of the seismic-active zone geometry, but also for understanding the processes that govern the flow of fluids along the damage zone. In this framework, we present the results of an integrated approach where geoelectrical and passive seismic data are used to construct a 3D geological model, whose simulated temporal evolution allowed the estimation of CO2 flux along an active fault in the area of Matese Ridge (Southern Apennines, Italy). By varying the geometry of the source system and the permeability values of the damage zone, characteristic times for the upward migration of CO2 through a thick layer of silts and clays have been estimated and CO2 fluxes comparable with the observed values in the investigated area have been predicted. These findings are promising for gas hazard, as they suggest that numerical simulations of different CO2 degassing scenarios could forecast possible critical variations in the amount of CO2 emitted near the fault.

Spectral analysis of the self-potential (SP) field for geometrically simple anomalous bodies is studied. In particular, three spectral techniques, i.e. Periodogram (PM), Multi Taper (MTM) and Maximum Entropy (MEM) methods, are proposed to derive the depth of the anomalous bodies. An extensive numerical analysis at varying the source parameters outlines that MEM is successful in determining the source depth with a percent error less than 5%. The application of the proposed spectral approach to the interpretation of field datasets has provided depth estimations of the SP anomaly sources in very good agreement with those obtained by other numerical methods.

The magnetotelluric (MT) method is useful for monitoring geophysical processes because of a large dynamic depth range. In this paper, a feasibility study of employing continuous MT observations to monitor hydrothermal systems for both volcanic hazard assessment and geothermal energy exploitation is presented. Sensitivity of the MT method has been studied by simulating spatial and temporal evolution of temperature and gas saturation distributions in a hydrothermal system, and by calculating the MT response at different time steps. Two possible scenarios have been considered: the first is related to an increase in the fluid flow rate at the system source, the second is associated to an increase in the permeability of the rocks hosting the hydrothermal system. Numerical simulations have been performed for each scenario, and the sensitivity of the MT monitoring has been analyzed by evaluating the time interval needed to observe significant variations in the MT response. This study has been applied to the hydrothermal system of the Campi Flegrei (southern Italy) and it has shown that continuous MT measurements are not sensitive enough to detect a significant increase in the source fluid flow rate over time intervals less than ten years. On the contrary, if the permeability of the upwelling zone increases, a measurable change in the MT response occurs over a time interval ranging from six months to three years, depending on the extent of the permeability increase. Such findings are promising and suggest that continuous MT observations in active volcanic areas can be useful for imaging volcano-hydrothermal system activity.

The emptying of a magma chamber can trigger eruptive series of events that can be very different in duration and explosivity degree. Usually, erupted magma is a mixture of magmas that originate at various depths and can significantly affect the style of the eruptive processes. In this work, possible correlations between depth of origin of magma and eruption size are investigated using a cellular automaton model that describes magma ascent in a buoyancy field as a diffusive dynamics on self-organized fracture networks. Interestingly, the model predicts that erupted magma is, generally, a mixture of magma that has continuously stopped during the whole ascent path from the chamber to the surface, except for eruptions above a given size threshold, for which it is possible to distinguish two dominant components deriving from specific depth ranges. Such a finding can provide a theoretical framework for the general feature of many volcanic eruptions whose deposits are characterized by two different magmas. Furthermore, in the repose times distribution, a timescale separation between short and long more probable repose times is found, which increases by deepening of the magma chamber. The identification of two different types of repose times suggests the presence of different patterns, which could help the understanding of magmatic processes responsible of different eruptive regimes that may characterize the life of a volcano.

Phaistos is one of the main Minoan palatial sites in the Aegean context. The ancient sources and the scarce archaeological records indicate that the city, destroyed by the nearby town of Gorthina during second century bc, underwent also an important post-palatine phase from alto-archaic and archaic age to Hellenistic age, still scarcely excavated. Thus, in recent years, new research that integrate different methodologies has been performed to study part of the western Mesara plain, with a special focus on the area around the Phaistos’ hills and the underlying plateau. In this paper, the results of high-resolution magnetic, electromagnetic, electrical and aerial photogrammetric surveys carried out in an area south of Phaistos site (Haghios Ioannis) are reported. All geophysical and geomatic data are consistent in showing a sharp contrast in soil properties of the northern and southern sectors of the survey area, which could help the paleoenvironmental reconstruction of the large lake developed at the foot of the Phaistos hill during the Minoan age. Furthermore, several deep and shallow anomalies with regular geometric shapes suggest the presence of different types of buried structures. In particular, in the shallowest part of the subsoil, all observed data show elongated structures, whose geometry and physical properties well correlate with the presence of wall remains, and an anomaly feature with a semicircular shape also detected by previous orthophoto interpretation studies. Copyright © 2016 John Wiley & Sons, Ltd.

A new approach to the interpretation of magnetic anomalies generated by geological structures resembling simple geometrical bodies is presented. The method is based on the Genetic-Price hybrid Algorithm (GPA) recently proposed by the authors for the inversion of potential-field data, specifically of self-potential signals. In this paper, the effectiveness of the proposed algorithm is tested on magnetic data for retrieving the parameters of the anomaly causative sources. First, the testing is performed on synthetic noise-free and noisy signals due to magnetized sphere-, dike- and fault-like models, then the analysis is extended to field magnetic anomalies. Concerning the synthetic data analyses, a very good agreement is obtained between assumed and retrieved source parameters. Specifically, the error between true and inverted parameter sets was found to be no higher than 9% even by adding 15% of Gaussian white random noise to the initial dataset. As for the study of field data, the values of depth, horizontal position, effective magnetization intensity and angle provided by the proposed GPA method compare well with those obtained by other interpretative approaches. Finally, the results of the GPA application to the inversion of magnetic data measured in the Mt. Somma-Vesuvius volcanic area are reported. In particular, the interpretation of the magnetic anomalies along a SW-NE profile in terms of multiple dikes provides information about depth and location of buried volcanic structures that match well those from other geophysical and geological analyses.

This study combines geological, geotechnical and geophysical analyses to construct a 3D model of the subsoil, based on a significant amount of data collected at a test site in the Lattari mountain range in southern Italy. Several investigation techniques were applied to study the spatial variability of the local stratigraphic setting and relevant geophysical and geotechnical parameters at the slope scale. By integrating field and laboratory measurements, such as geological logs, soil sampling, 2D and 3D electrical resistivity tomography surveys, time domain reflectometry profiles and characteristic curves of electrical resistivity vs saturation degree, the continuous stratigraphy of the pyroclastic cover in question was reconstructed and 3D models of effective saturation degree in different pyroclastic horizons were retrieved. As the proposed procedure is applied to study flowslide initiation, the test site was implemented on a 32° slope affected by historical flowslides. The following factors predisposing to flowslides were identified: steep stratigraphic contact between shallower soils and bedrock, impermeable layers that buffer the draining of the fractured limestone, preferential rainwater infiltration paths, and spatial distribution of water content in the soil.

Integration of archaeoseismic observations, geological and geophysical surveys and a critical review of historical written sources contributed to shed light on the effects of the 847 CE earthquake that struck a large area of southern-central Italy. New archaeoseismic evidence of a strong earthquake which occurred around the middle of the ninth century CE comes from two Medieval archaeological sites along the Volturno Valley, between Campania and Molise regions. Historical documentation and its evidence include the tilting of pillars in the Basilica of Santa Maria near Alvignano (northern Campania region) and a collapsed masonry wall in the Abbey of San Vincenzo al Volturno near Isernia (northern Molise region). At Alvignano, a site so far unrecorded in seismic catalogues for the 847 earthquake, geoelectrical and georadar investigations were used to explore the subsoil and study local site conditions, which could have influenced co-seismic ground motion. Interpretation of geophysical surveys calibrated by stratigraphy of available boreholes document the presence of altered pyroclastic deposits, which likely enhanced site effects at Alvignano. Analysis of damage historical descriptions and of archaeological reports indicates that the 847 seismic event damaged a wide area between Latium, Campania and Molise, with destruction of the town of Isernia and damages in Rome. Because the damaged area for this medieval earthquake is loosely defined in literature, the present study represents a contribution to better define the shaking area and provide new hints on the extent and location of the possible seismogenic source.

Soil suction and resistivity strongly depend on the degree of soil saturation and, therefore, both are used for estimating water content variations. The main difference between them is that soil suction is measured using tensiometers, which give point information, while resistivity is obtained by tomography surveys, which provide distributions of resistivity values in large volumes, although with less accuracy. In this paper, we have related soil suction to electrical resistivity with the aim of obtaining information about soil suction changes in large volumes, and not only for small areas around soil suction probes. We derived analytical relationships between soil matric suction and electrical resistivity by combining the empirical laws of van Genuchten and Archie. The obtained relationships were used to evaluate maps of soil suction values in different ashy layers originating in the explosive activity of the Mt Somma-Vesuvius volcano (southern Italy). Our findings provided a further example of the high potential of geophysical methods in contributing to more effective monitoring of soil stress conditions; this is of primary importance in areas where rainfall-induced landslides occur periodically.

The archaeological site of Nora is built on a large peninsula that marks the Gulf of Cagliari (Sardinia island, Italy) at its southernmost part. As for most of the ancient coastal cities, the study of Nora is rather complex because of the partial (or complete) burial or submergence of the archaeological remains and their degradation induced by the progressive seawater entry. In recent years, an extensive multi-disciplinary research project, which involves disciplines varying from archeology to earth sciences, has started. In particular, in this paper the results of a high-resolution multi-methodological geophysical prospecting focused on the coastal area between land and sea, not considered in previous urban and marine archaeological researches, are reported and integrated with new archaeological data. The interpretation of the geophysical anomalies is well supported by the results of an archaeological excavation in the north-western sector of the survey area and suggests, in the eastern (not excavated) sector, the presence of buried structures that could lead to redraw the urban settlement of the area, with housing blocks connected by paved open spaces. Furthermore, the geophysical survey clearly identifies the sharp lowering of the andesitic bedrock from the foot of the Coltellazzo hill to the west, offering fundamental data to reconstruct the paleo-environmental landscape of the ancient settlement.

Summary Debris-flows are among the most dangerous types of landslide often induced by intense rainfall events and favored by the presence of unconsolidated soils on steep slopes. In this framework, the knowledge of the electrical resistivity distribution of the cover materials is useful to define their lithological and physical characteristics, as they provide information about the saturation level of the cover and the areas more prone to slide. Nevertheless, the use of conventional electrode layouts for 3D-resistivity imaging is limited by the difficulty to operate in contexts with complex topography. In this work, a non-conventional 3D electrode configuration is proposed for a high-resolution characterization of Mt. Faito pyroclastic covers (Campania Region, southern Italy). The adopted scheme has been able to provide a very detailed image of the investigated slope allowing to discriminate the different horizons within the pyroclastic cover as well as the different degree of fracturing of the carbonate bedrock. The obtained resolution has been found higher than that provided by a conventional 2.5D ERT survey performed in the same survey area.

A multi-methodological geophysical prospecting was performed in a survey area of the archaeological Phaistos site on the Greek island of Crete, as part of an international research project aimed at investigating the less excavated hills of Phaistos and the underlying plateau. The article provides an assessment of the resolution of the chosen techniques for non-destructive testing of buried ancient structures in the geological landscape of Phaistos. The magnetic and electromagnetic surveys clearly detected anomalies related to human activity, some of which were subsequently defined in detail by resistivity tomography imaging. In particular, variations of the observed electrical and magnetic parameters perfectly correlate to a wall structure made of calcareous material, which has been brought to light by subsequent excavations that unearthed large sectors of a fortification in a double curtain wall, chronologically consistent with the historical sources about the destruction of Phaistos in 150 bc. Copyright © 2015 John Wiley & Sons, Ltd.

Hydrogeophysics has been developing in recent years to improve characterization and monitoring of saturated/unsaturated aquifers, through the combination of geophysical and hydrogeological methods. To exploit the potential benefits of this integration, definition of petrophysical relationships that allow the translation of geophysical data into hydrogeological parameters (and vice versa) is essential. In this paper, Archie's and van Genuchten's models, which relate electrical resistivity and hydraulic conductivity to the degree of saturation, are combined to obtain a closed-form link between hydraulic conductivity and electrical resistivity. Such an expression is used to characterize pyroclastic deposits covering Sarno Mountains (southern Italy), which are often affected by landslide phenomena. As expected, an inverse relationship is found between hydraulic conductivity and electrical resistivity due to predominance of the electrolytic component over the interfacial component of the electrical conductivity. The variability of the hydraulic conductivity marks out the texture of the investigated pyroclastic horizons. Finally, a hydraulic conductivity–matric suction relationship is retrieved by using an empirical correlation between electrical resistivity and matric suction.

The thermal evolution of the Phlegraean magmatic system (southern Italy) is studied by analyzing the influence of the thermal property variations on the solution of the heat conduction equation. The aim of this paper is to verify if appropriate choices of thermal parameters can reproduce, at least to greater depths, the high temperatures measured in the geothermal wells, drilled inside the caldera, under the assumption of heat loss from a magma chamber by conduction. Since the main purpose is to verify the plausibility of such an assumption, rather simple models of the magmatic system are adopted and only major volcanic events (i.e., the Campanian Ignimbrite and the Neapolitan Yellow Tuff eruptions) are considered. The results of the simulated two-dimensional model scenarios show that by assuming an extended source region, whose emplacement time is longer than 40 ka, heat conduction mechanisms can provide temperatures as high as those measured at depths deeper than about 2000 m. On the other hand, the 1D simulations show that appropriate choices for the thermal conductivity depth profiles can reproduce the observed temperatures at depths deeper than about 1000 m. These findings question the apparent consensus that convection is the only dominant form of heat transfer at Phlegraean Fields and might motivate new research for reconstructing the thermal evolution of the Phlegraean magmatic system.

As it is well-known, the characterization plan of an old landfill site is the first stage of the project for the treatment and reclamation of contaminated lands. It is a preliminary in-situ study, with collection of data related to pollution phenomena, and is aimed at defining the physical properties and the geometry of fill materials as well as the possible migration paths of pollutants to the surrounding environmental targets (subsoil and groundwater). To properly evaluate the extent and potential for subsoil contamination, waste volume and possible leachate emissions from the landfill have to be assessed. In such perspective, the integrated use of geophysical methods is an important tool as it allows a detailed 3D representation of the whole system, i.e. waste body and hosting environment (surrounding rocks). This paper presents a very accurate physical and structural characterization of an old landfill and encasing rocks obtained by an integrated analysis of data coming from a multi-methodological geophysical exploration. Moreover, drillings were carried out for waste sampling and characterization of the landfill body, as well as for calibration of the geophysical modeling.

Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

Soil response to rainfall is a complex phenomenon that requires modeling of many sources of heterogeneity, whose variations can be relevant on various timescales and whose precise description requires a large amount of data inputs. Due to the great complexity of the problem, many simplifying assumptions are usually made in modeling landslides triggered by rainfall. As regards rainfall-induced shallow landslides, conventional approaches base slope stability analyses on the infinite slope model combined with hydrological models, which provide the time evolution of groundwater pressure head and volumetric water content. On the other hand, the response of geophysical quantities to water changes depends also on the variations in mechanical and hydrological properties. For this reason, we attempt a different approach to the problem of slope stability assessment by shifting the focus on the analysis of variations in geophysical properties. In this paper, starting from experimental resistivity data acquired in a test area, we perform a series of numerical simulations to study how changes in soil resistivity spatial distributions may affect the size of unstable areas. We use a simple cellular automaton whose states are defined by the values of a local and time-dependent geophysical factor of safety, which depends on soil electrical resistivity and slope inclination. We studied the probability of occurrence of rainfall-induced shallow landslide events by driving the system to instability through a decrease in electrical resistivity values. Numerical simulations are performed by varying number and intensity of the applied perturbations. Hazard scenarios obtained by in situ distributions of resistivity values are compared with those coming from initial random distributed resistivity values. Our results suggest possible critical rates of resistivity changes for triggering instability in the investigated area and point out the crucial role of resistivity variations in prediction of larger events.

We simulated the pattern of activity of a strato-volcano by using a cellular automaton model where magma is allowed to ascend to the surface through self-organized crack networks. Magma rises toward the surface by filling connected paths of fractures until the magma's density is less than that of surrounding rocks. If magma enters a region with negative or neutral buoyancy, it cools and solidifies; as a result, the local density profile is modified, and magmatic dikes are formed. We simulated the temporal evolution of high-density pathways of dikes that magma may eventually utilize to reach the surface. We showed that if a shallow neutral–negative buoyancy zone is restored after eruptions, due to, for example, piecemeal or chaotic collapses, a characteristic timescale appears in the inter-event repose time distribution. Such characteristic repose time represents the average time that magma takes to form a high-density pathway through the less dense rock layer, and it may give a hint to predict possible eruptive scenarios. Even if the model includes many simplifying assumptions in the definition of magma–rock interaction, the results obtained from simulations are consistent with the eruptive behavior of the Mt. Somma-Vesuvius volcano for the 1631–1944 period.

An integrated approach to interpret Self-Potential (SP) anomalies based on spectral analysis and tomographic methods is presented. The Maximum Entropy Method (MEM) is used for providing accurate estimates of the depth of the anomaly source. The 2-D tomographic inversion technique, based on the underground charge occurrence probability (COP) function, is, then, used to fully characterize the anomalous body, as the MEM is not helpful in delineating the shape of the anomaly source. The proposed integrated approach is applied for the inversion of synthetic SP data generated by geometrically simple anomalous bodies, such as cylinders and inclined sheets. This numerical study has allowed the determination of mathematical relationships between zero lines of the COP distributions, the polarization angles and the positions along the profile of the causative sources, which have been of great help for interpreting the related SP anomalies. Finally, the analysis of field examples shows the high potential applicability of the proposed integrated approach for SP data inversion.

A global optimization method based on a Genetic-Price hybrid Algorithm (GPA) is proposed for identifying the source parameters of self-potential (SP) anomalies. The effectiveness of the proposed approach is tested on synthetic SP data generated by simple polarized structures, like sphere, vertical cylinder, horizontal cylinder and inclined sheet. An extensive numerical analysis on signals affected by different percentage of white Gaussian random noise shows that the GPA is able to provide fast and accurate estimations of the true parameters in all tested examples. In particular, the calculation of the root-mean squared error between the true and inverted SP parameter sets is found to be crucial for the identification of the source anomaly shape. Finally, applications of the GPA to self-potential field data are presented and discussed in light of the results provided by other sophisticated inversion methods. A hybrid algorithm is proposed for self-potential data inversion.Features of Controlled Random Search and Genetic Algorithms are combined.Accurate estimations of the SP source parameters are obtained.The proposed method is able to discriminate the shape of the anomaly source.

In the territory of the Campania region (southern Italy), critical rainfall events periodically trigger dangerous fast slope movements involving ashy and pyroclastic soils originated by the explosive phases of the Mt. Somma-Vesuvius volcano and deposited along the surrounding mountain ranges. In this paper, an integration of engineering-geological and geophysical measurements is presented to characterize unsaturated pyroclastic samples collected in a test area on the Sarno Mountains (Salerno and Avellino provinces, Campania region). The laboratory analyses were aimed at defining both soil water retention and electrical resistivity curves versus water content. From the matching of the experimental data, a direct relationship between electrical resistivity and matric suction is retrieved for the investigated soil horizons typical of a ash-fall pyroclastic succession. The obtained relation turns out to be helpful in characterizing soils up to close saturation, which is a critical condition for the trigger of slope failure. In such a regime, the water content and the matric suction have small variations, while electrical resistivity variations can be appreciated in a larger range of values. For this reason, besides suction measurements on very small soil volumes through classical tensiometers, our analyses suggest the direct monitoring of in-situ electrical resistivity values as an effective tool to recognise the hydrological state of larger and more representative soil volumes and to improve early warning of dangerous slope movements., 23 pages, 10 figures, 2 tables

Volcanoes are complex dynamical systems that manifest their activity in a wide range of eruptions with different explosivity. In this paper, we propose a cellular automaton model aimed at capturing the key features of the magma ascent dynamics that reproduces the great variability of volcanic explosivity. The novelty of our approach consists in considering an eruption not as an event caused by overpressure in the magma chamber but as the result of transport of discrete magma batches from the reservoir to the surface along preformed crack networks. Since gas is lost during the ascent, the time for the rise of magma is crucial. In our model, such a time is related to the opening of connected paths of fractures in the surrounding rocks, which are described by means of a time dependent Self-Organized-Criticality field. We analyze statistical distributions of eruptions in terms of gas losses and find that eruptions with the lowest degree of explosiveness are characterized by small erupted volumes, even if they are much more likely to occur than explosive eruptions. On the contrary, explosive eruptions, which in our model essentially correspond to percolative events that directly connect the magma reservoir to the surface, are rare events that can be very huge.

Landslides are natural hazards occurring in response to triggers of different origins, which can act with different intensities and durations. Despite the variety of conditions that cause a landslide, the analysis of landslide inventories has shown that landslide events associated with different triggers can be characterized by the same probability distribution. We studied a cellular automaton, able to reproduce the landslide frequency-size distributions from catalogues. From the comparison between our synthetic probability distribution and the landslide area probability distribution of three landslide inventories, we estimated the typical size of a single cell of our cellular automaton model to be from 35–100 m2, which is important information if we are interested in monitoring a test area. To determine the probability of occurrence of a landslide of size s, we show that it is crucial to get information about the rate at which the system is approaching instability rather than the nature of the trigger. By varying such a driving rate, we find how the probability distribution changes and, in correspondence, how the size and the lifetime of the most probable events evolve. We also introduce a landslide-event magnitude scale based on the driving rate. Large values of the proposed intensity scale are related to landslide events with a fast approach to instability in a long distance of time, while small values are related to landslide events close together in time and approaching instability slowly.

In this paper, we propose a new approach for modelling the development of instabilities in pyroclastic covers induced by rainfall events. The approach is based on high-resolution 2D electrical resistivity tomography (ERT) and on a cellular automaton model aimed to simulate landslide events. A local and time-dependent Factor of Safety of a slope, that depends on electrical resistivity and the slope angle, is introduced. We show the results of a model run on data acquired in a test area of the Campania Region (Italy), where shallow landslides involving pyroclastic soils are very frequent and often triggered by critical rainfall events. The shown example would just emphasize the potentiality of the proposed conceptual model, which integrates geophysical and statistical analyses, in the framework of the landslide hazard, since its validation and calibration is the next step that has to be taken.

In the last few decades, the spectral analysis approach has been successfully applied for depth estimation of gravity and magnetic anomaly sources by analyzing the signal power distribution as a function of spatial frequencies. In the present work, application of high-resolution spectral methods is proposed for inversion of self-potential (SP) data. In particular, Periodogram Method (PM), Maximum Entropy Method (MEM) and Multi Taper Method (MTM) are used to invert synthetic SP data generated by cylinder and sheet sources. From analysis, MEM was found to be better in providing more accurate estimates of the source depth as compared to PM and MTM. Finally, the application of the proposed methods to field data is presented and the estimated depths are compared with those obtained by other numerical methods.

In the last decades, infrared thermography has been successfully applied to various materials and structures for the assessment of their state of conservation and planning suitable restoration works. To this aim, mathematical models are required to characterize thermal anomaly sources, such as detachments, water infiltration and material decomposition processes. In this paper, an algorithm based on the conservative finite difference method is used to analyse pulse thermography data acquired on an ancient building in the Pompeii archaeological site (Naples, Italy). The numerical study is applied to both broad and narrow elongated thermal anomalies. In particular, from the comparison between simulated and experimental thermal decays, the plaster thickness was characterized in terms of thermal properties and areas of possible future detachments, and moisture infiltration depths were identified.

We review some results concerning the superconducting properties of a 2D superconductor in the proximity to an electronic topological transition (ETT). In contrast to the 3D case, we find that the superconducting gap at T = 0 is characterized by a nonmonotonic behavior, with maxima occurring close to the ETT, both for s- and for d-wave pairing. Such a result is in good qualitative agreement with the phenomenological trend recently observed for [Formula: see text] as a function of the hopping ratio t′/t for several cuprate compounds. We also derive a nonmonotonic dependence of the quasiparticle inverse lifetime due to impurity scattering. We further analyze the effect of an ETT on the Ginzburg-Landau stiffness η, for which we recover an expression analogous to the case of an isotropic dispersion relation.

The coastal alluvial plain of Sarno River (Campania Region, southern Italy) is a very rich environment that has experienced a long history of changes due to both natural phenomena such as eustatic sea-level variations and deposition of volcanoclastic sediments, and human civilizations who populated this area since historical times. As a result, it is characterized by complex stratigraphic sequences and groundwater flow systems. The architecture of the multi-layered aquifer system in a sample area, located in a densely urbanized sector at the mouth of Sarno River, was reconstructed. Starting from the analysis of stratigraphic log data and laboratory geotechnical measurements, the lithostratigraphical-unit sequence was retrieved and a realistic three-dimensional (3D) model of the hydrogeological heterogeneity was obtained. The results of a detailed 2D electrical resistivity tomography survey were used to support the analysis of the spatial heterogeneity of the aquifer system in a sector characterized by lack of log data. The integration of hydrogeological and geophysical data allowed for the reconstruction of a 3D hydrogeophysical model of the multi-layered system, which electrically characterizes and geometrically identifies two aquifers. Finally, piezometric-level measurements validated the hydrogeological–geophysical model and showed the effectiveness of the methodology.

The knowledge of the geological setting of pyroclastic covers and their water content distribution represents crucial information for stability analyses of slopes potentially subject to debris-flow phenomena. The study we here present would provide a contribution to this issue by means of an approach based on electrical resistivity measurements. Specifically, we describe the results of high-resolution 2D resistivity surveys carried out in a test area on Sarno Mountains (Campania Region – Southern Italy), where shallow landslides involving pyroclastic soils periodically occur triggered by critical rainfall events. We discuss the results in relation to the geology of the area in order to locate characteristic horizons of pyroclastic soils below the ground surface. Then, on the basis of a laboratory characterization of pyroclastic samples collected from the same test area at representative depths, we provide an estimation of the soil water content distribution in the field. Finally, we analyze temporal variations of the soil water content distribution by comparing the data of two surveys carried out in the autumnal and spring seasons, respectively.