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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.

This study provides deeper insights on our knowledge of the 847 AD earthquake that struck a large area in Central-Southern Italy causing diffuse damages in northern Campania, Molise, and Latium. Through the integration of archaeoseismic observations, geological and geophysical surveys together with a critical review of historical written sources, it was possible to identify evidence of seismic effects on ancient Medieval buildings or structures uncovered during archaeological excavations.

Deltas are crucial places for life and economic development of a country, harbouring small towns and megacities, feeding agricultural activities and fishing, and hosting important ecosystems. These complex natural systems, although modified over the centuries by human interventions, are founded on a delicate water-land balance that strongly depends on climatic variability. The seasonal and periodic components of the water cycle are well studied, but the influence of the hydrological processes on the periodic signals exhibited by the geodetic data is poorly understood. The new procedure we propose aims at understanding the influence of climate-driven hydrological processes on the dynamics of the Earth’s surface. It is part of a wider innovative methodological approach addressed to retrieve both periodic and permanent components of the vertical ground motion and aimed to predict more accurate subsidence rates, especially in the most vulnerable delta regions. It can be seen as a multi-step procedure: standard statistical methods and wavelet analysis are first applied to geodetic time series for extracting the permanent and periodic components (Step 1). Then, comparative analyses, performed through standard linear and non-linear techniques (e.g. cross-wavelet transform and wavelet transform coherence analyses), are used for comparing geodetic data with datasets of different nature (e.g., meteorological, hydrological, hydrogeological, mareographic and weather data), in order to find correlation between land and hydrologic system and to infer possible processes or sources (Step 2). Finally, the sources are validated through physically-based models (Step 3). Compared to qualitative or semi-quantitative methods, the defined procedure allows to overcome the routinely used one-source investigation and improves the relation between data analysis and physically-based modelling. Moreover, differently from other approaches, that validate the source from the model findings, the procedure spots the relevance of the source from data analysis reducing the ambiguity in modelling. The new methodology has been applied to geodetic and hydro-meteorological data of the Po Delta Area (Northern Italy) over 2009 - 2017 period. It has led to identify the main water-dependent processes that control the annual oscillation of the vertical ground movements. In particular, it turns out that the periodic annual component of the vertical ground motion exhibited by CGPS time series is explained by soil moisture mass change in the southern part of the Delta and by river water mass change in the central one.

In the last twenty years a growing interest is noticed in studying the process of carbon dioxide degassing from the geosphere in various field of Earth Sciences, with the aim of defining relationship between gas flux on the ground and tectonic structures, quantifying deeply derived CO2 released into the atmosphere and studying volcanic and non-volcanic degassing processes (Frondini et al., 2018). While it is already recognized that volcanic degassing can introduce huge quantities of carbon dioxide into the atmosphere, only recent studies (Kerrick and Seward, 1996; Chiodini et al., 2000) have shown that non-volcanic degassing may be a globally significant input of CO2 into the atmosphere and that many areas where large emissions of non-volcanic carbon dioxide take place are also characterized by a some degree of permeability of the crust that often coincides with seismically active zones. Indeed, seismic data demonstrate the existence of a positive spatial correlation between gas discharges and extensional tectonic regimes and confirms that such processes would play a key role in creating pathways for the rising gases at micro- and macro-scales, increasing the rock permeability and connecting the deep crust to the earth surface. Therefore, the identification and the geometric characterization of the CO2-permeable active faults is fundamental, not only for the definition of the seismic-active zone geometry, but also for the understanding of the processes that govern the flow of fluids along the damage zone that convey the gases towards the surface. In this framework, numerical simulations of fluid flow in three-dimensional faulted models can help to identify the primary control parameters of fault-related fluid flow, their interactions, and to model the temporal evolution of the investigated system. In this work, we present the results of a numerical simulation of CO2 flow along an active fault in the Ciorlano area of Matese Ridge (Southern Apennines, Italy), which recent accurate geological and geochemical analyses classify as the area with the highest non-volcanic natural emissions of CO2 ever measured on Earth (Ascione et al., 2018).

Monitoring hydrothermal systems is of crucial interest for different applications, such as volcanic hazard assessment and geothermal energy exploitation. In this work, the feasibility of using continuous magnetotelluric (MT) measurements for monitoring hydrothermal systems developing within active volcanoes is investigated. The MT method is indeed a good candidate for characterizing the dynamics of a volcano-hydrothermal system as it is suitable for deep exploration of the subsoil (from one hundred meters to hundreds of kilometers) in terms of electrical resistivity values, which are strongly sensitive to variations in temperature and underground fluids distributions. To test the proposed methodology, a reliable model of the Campi Flegrei (Southern Italy) volcanic complex based on geochemical and geophysical data is built and a sensitivity study is carried out by changing the fluid flow rate of the hydrothermal source and the permeability distribution of the system. In particular, for each evolution scenario the MT sensitivity has been studied by evaluating the time intervals needed for observing significant resistivity variations. The simulations have shown that the MT monitoring is much more sensitive to changes in rock permeability rather than in the fluid flow rate emitted by the source. In general, time intervals not useful for volcano monitoring purposes (i.e., longer than 10 years) are found if only changes in fluid flow rate are assumed to govern the hydrothermal system dynamics. Conversely, by increasing the permeability of the hosting rocks up to about one order of magnitude, consistent resistivity variations are observed over a period ranging from one year and a half to three months. Such findings are promising and encourage the use of the continuous MT measurements in active volcano-hydrothermal areas.

We study the statistical features of eruptive events causing the emptying of a closed magma chamber by using a discrete dynamical system where magma spreads through self- organized fracture networks. Specifically, the complex interactions between magma and rock are modelled by means of three dynamical variables that, respectively, control the occurrence of self-organized crack networks, the magma diffusion and the formation of high-density dikes, which magma uses to eventually reach the surface. In particular, the numerical analysis has been carried out for investigating the main features of erupted volumes and repose times statistical distributions. 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 distribution of repose times 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 the processes responsible for different eruptive regimes.

In the last decades, an increasing number of global optimization algorithms has been proposed to solve geophysical inverse problems. Indeed, an inverse problem can be posed as an optimization problem where the function to be optimized, usually called objective function, misfit function or fitness, provides an estimate of the difference between observed data and synthetic data computed by a trial model. In the framework of probabilistic global optimization methods, some algorithms use statistical distributions inspired by physical processes to get suggestion on which solution candidate has to be tested next, as for example the Simulated Annealing algorithms, which select the next solution candidate according to the Boltzmann probability factor of atom configurations of cooling metals. Other algorithms, such as the Genetic Algorithms, are instead inspired by Darwinian evolution processes and treat solution candidates as individuals that compete in a virtual environment controlled by the genetic mechanisms of mutation, reproduction and crossover. In this work, we present and discuss some applications of a new hybrid global optimization algorithm, GPA, recently proposed by the authors for quantitative interpretations of potential field data. The proposed approach includes a mutation operator in a controlled random search algorithm and its effectiveness has been proved on several synthetic and field data concerning single self-potential (SP) anomalies. Here, the results of a numerical study focused on multiple self-potential anomalies are shown for two of the main application fields of the SP method, i.e. for volcanic and soil contamination risk.

Suitable estimation of the land subsidence is crucial in deltas and coastal areas, where the climate variability effects (e.g., frequent and intense rain storms, peaks in river discharge and sea level fluctuations), coupled with natural or anthropogenic land sinking, represent serious factors of inundation risk. Nowadays, continuous GNSS (Global Navigation Satellite Systems) networks have become precision monitoring tools of the ground displacement. Customarely GNSS site-position time series exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. These periodic variations may be due to the joint contributions of surface mass redistribution (atmosphere, ocean, snow and soil moisture), frequently masked by the superposition of several correlated or uncorrelated noise sources. These signals have to be recognised and properly modelled in order to estimate the vertical ground lowering. Several authors investigate the seasonal component by using the “peering approach”, which is based on the comparison between the joint contribution of established individual geophysical sources (not removed during the data processing phase) and the observed seasonal variations. This approach allows to quantify influence, distribution and magnitude of the individual sources and to understand the main processes affecting the geodetic time series. Differently from this approach, we propose a procedure based on two steps: multi-disciplinary and multi-methodological comparative analyses are first performed for selecting the physical mechanism (individual source) that better explain the seasonal signals clearly exhibited by the GPS time series. Then, a physically-based model is used for enhancing the extraction of the geodetic trend and thus estimating the geodetic velocity without seasonal oscillations. The proposed approach is applied to the Codigoro test site (southern part of the Po Delta – Northern Italy), which is an area historically affected by both anthropogenic and natural subsidence and influenced by climatic changes.

In the last decades, the magnetotelluric (MT) method has been proved to be a useful geophysical tool in different contexts, from geothermal reservoir characterization to crustal structures studies. Nevertheless, the MT method is very sensitive to the presence of noise. Indeed, as the method is based on the measurement of both the electric and magnetic components of the natural electromagnetic (EM) field, it fails when these components are affected by noises of different nature. In particular, in industrialized and urbanized context, the MT time series could be strongly affected by man-made noise and, as a consequence, the impedance tensor estimates, given by the ratio between the electric and magnetic components of the MT field, could be unreliable. To improve the reliability of these estimates, most of the proposed approaches rely on the robust evaluation of the impedance tensor as well as on the use of remote reference MT stations or on the combination of both approaches. However, these methods are not always effective. The robust methods fail when most of the data are affected by noise, giving as result a biased impedance tensor, while the remote reference approach is ineffective when the noise is correlated between reference and local MT station. In recent years, alternative procedures have been proposed to obtain reliable estimates of the MT impedance tensor. In the present work, a different approach based on the use of Discrete Wavelet Transform (DWT) and Self-Organizing Map (SOM) neural network analysis is proposed for improving the magnetotelluric impedance tensor estimates. The approach has been tested by changing type, level and window length of the noise affecting MT time series. Furthermore, in order to identify the most reliable apparent resistivity and phase values among the different impedance tensors clusters provided by the SOM analysis for each analyzed period, a selection criterion is provided and tested on synthetic and field MT data.

Background To investigate the effects of chronic administration of ranolazine (RAN) on experimental model of heart failure with preserved ejection fraction. Methods Seven-weeks old Dahl salt-sensitive rats were fed a high salt diet for 5weeks to induce hypertension. Afterwards, rats continued with a high salt diet and were administered either with vehicle or RAN (20mg/kg/die, ip) for the following 8weeks. Control rats were maintained on a low salt diet. Results While systolic parameters were not altered, diastolic parameters were changed in high salt animals. Hemodynamic analysis showed a decreased dP/dt min, increased LVEDP, longer time constant and steeper slope of the end-diastolic pressure–volume relationship. Treatment with RAN attenuated these alterations and determined a reduction in mortality. Additionally, the magnitude of myocardial hypertrophy and activation of PI3K/Akt pathway were reduced. Alteration in diastolic compliance as a consequence of elevated myocardial stiffness was confirmed by an increase of collagen deposition and activation of pro-fibrotic TGF-β/SMAD3/CTGF signaling. These effects were counteracted by RAN. High salt rats had a decrease in SERCA2 and an increase in Na + /Ca 2+ exchanger (NCX). Treatment with RAN reduced NCX expression and determined an increment of SERCA2. Moreover, the levels of nitrotyrosine and oxidized dyhydroethidium were higher in high salt rats. RAN induced a decrement of oxidative stress, supporting the concept that reduction in ROS may mediate beneficial effects. Conclusions Our findings support the possibility that diastolic dysfunction can be attenuated by RAN, indicating its ability to affect active relaxation and passive diastolic compliance.

The recurrence time statistics of a cellular automaton modelling landslide events is analyzed by performing a numerical analysis in the parameter space and estimating Fano factor behaviors. Simulation results are qualitatively compared with those from a recent analysis performed by Witt et al.(Earth Surf. Process. Landforms, 35, 1138, 2010) for the first complete databases of landslide occurrences over a period as large as fifty years. From the comparison with the extensive landslide data set, the numerical analysis suggests that statistics of such landslide data seem to be described by a crossover region between a correlated regime and an uncorrelated regime, where recurrence time distributions are characterized by power-law and Weibull behaviors for short and long return times, respectively. Finally, in such a region of the parameter space, clear indications of temporal correlations and clustering by the Fano factor behaviors support, at least in part, the analysis performed by Witt et al. (2010).

The structure of a volcanic edifice depends on composition, volume of erupted magma, and mechanism of eruption. In particular, pressure variations with depth due to density variations of the volcanic rocks are crucial for magma degassing processes as well as buoyancy mechanisms. In this paper, we analyze the effects of three different pressure profiles with depth on the probability of occurrence of simulated eruptive events. We describe the dynamics of magma ascent by means of a time dependent Self-Organized-Criticality field, which controls the opening of crack networks through which discrete magma batches are allowed to rise. We find a characteristic power-law behavior for the number of eruptions with erupted volume in all considered cases. As concerns the probability of occurrence of an eruptive event with a given percentage of gas losses, we find that extreme events, i.e., events with the lowest percentage of gas losses, are more likely to occur if a step function is used to describe the relationship between rock density and depth.

We simulate the volcanic activity of a basaltic stratovolcano by using a cellular automaton model where magma is allowed to rise through self-organized crack networks. Magma rises toward the surface by filling connected paths of fractures until the magma’s density is less than, or equal to that of the surrounding rocks. If magma enters a less dense rock layer, it cools and thus solidifies; as a result, the local density profile is modified, and solid filled dikes are formed. We simulate the temporal evolution of such high density pathway of dikes which magma may eventually utilize to reach the surface with the occurrence of an eruption. Magma degassing is also taken into account by means of the relationship between the pressure-controlled water solubility and the lithostatic pressure. We study the statistical properties of the automaton by varying the model parameters and, in particular, the thickness of the uppermost rock layer, which controls the buoyancy rate of magma rise because of its low value of density. We show that, if the initial rock density profile is restored after each eruption because, for example, piecemeal or chaotic collapses, a characteristic timescale appears in the inter-event repose time distribution, which represents the average time that magma takes to form an high density pathway through the less dense rock layer. An application of the model to the statistics of the eruptive activity of the Somma-Vesuvius volcano for the 1631-1944 period is discussed.

Recognition of the framework of complex aquifer systems is a key topic to analyse groundwater conditions and contaminant transport in heterogeneous and anisotropic aquifers. Groundwater models are useful tools for management and protection of water resources, but their predictive function is often constrained by a deficient characterization of hydrogeological parameters at a local scale. A wide variety of structural, volcanic and depositional processes produce a composite spatial distribution of hydraulic conductivity in alluvial aquifer systems. Groundwater flow and solute transport simulation in similar multilayer systems of several aquifers, aquitards and aquiclude needs a vast amount of observed data. Many tools for the knowledge of lithologic and stratigraphic features and of spatial heterogeneity at different scales are available, but it is essential to improve the hydrofacies models including in situ testing procedures that would better treat the rock strata connectivity and the geometry of aquifers. With regard to coastal alluvial aquifer system of the Sarno River plain (South Italy), this paper points out a multidisciplinary approach to delineate aquifer/aquitard unit and depict a realistic 3D modelling of the hydrogeological heterogeneity using geological, hydrogeological and geophysical techniques. Namely, due to its composite hydrodynamic conditions and to the high human impact, it has been chosen the sector of this plain located at the river mouth. Accordingly, this paper presents some interesting aspects to quantify the relationship between geophysical parameters and hydrostratigraphic properties of a highly heterogeneous alluvial aquifer system.

In questo lavoro si propone un metodo non-invasivo basato su un’interpretazione congiunta di misure di resistività elettrica di sito e di laboratorio, finalizzato sia alla conoscenza dell’assetto geologico di coltri piroclastiche sia alla determinazione della distribuzione del contenuto d’acqua al loro interno. I risultati di un’applicazione del metodo a un’area test dei Mt. di Sarno (Salerno, Italy), evidenziano le potenzialità dell’approccio proposto nell’analisi di stabilità di versanti potenzialmente soggetti a fenomeni franosi indotti da piogge, e suggeriscono un monitoraggio in continuo di tale parametro elettrico come uno strumento efficace per la definizione di soglie di allarme di potenziali frane di tipo debris-flow.