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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.

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Flow-like landslides, which occur mainly in shallow granular deposits resting on steep bedrock, represent a major natural hazard worldwide. The pore water pressure distribution and the soil water content directly affect the soil shear strength, thus controlling the triggering of these landslides. Criticalgeomorphological and topographical settings, together with peculiar stratigraphic and hydrogeological features, are commonly recognized as predisposing factors for flow-like landslides occurrence. Hence, investigating the spatial and temporal variability of hydraulic slope conditions is a fundamental activity that consists of identifying local geological factors and seasonal monitoring of the subsurface water regime. The present work proposes an integrated geological, geophysical and geotechnical approach to identify the spatial variability of the local stratigraphic setting and hydrogeological conditions in a partially saturated slope, in order to set up a procedure able to provide a prediction of the flow-like landslides occurrence atslope scale. The multidisciplinary study has been applied to a test site on Mt. Faito, in the Lattari Mts. (Southern Italy), where extensive geophysical, geological and geotechnical soil characterization and in situmonitoring data collected over two years are available.

<p>The study of flooding events resulting from bank over-flooding and levee breaching is of large interest for both society and environment, because flood waves, resulting from levee failure, might cause loss of lives and destruction of properties and ecosystems. Understanding the subsoil mechanics and the fluid-solid interplay allows the stability condition estimate of dams, embankments and slopes and the development of early warning alarm systems. Changes in soil and hydraulic parameters are usually monitored by geotechnical and geophysical investigations that also provide the basic assumptions for developing hydraulic models. Nowadays, remote sensing approaches, including satellite techniques, are mainly used for flooding simulation studies. Indeed, remote sensing observations, such as discharge, flood area extent and water stage, have been used for retrieving flood hydrology information and modeling, calibrating and validating hydrodynamic models, improving model structures and developing data assimilation models. Although all these studies have contributed significantly to the recent advances, uncertainty in observations, as well as in model parameters and prediction, represents a critical aspect for using remote sensing data in the flooding defence. Compared to past and current methods for monitoring the fluvial levee failure, we propose a new procedure that provides a wide and fast alert system. The proposed methodological path is based on presumed relationships between ground level deformation and hydrological and surface soil properties, due to physical mechanisms and exhibited by geodetic and hydrological time series. The procedure is accomplished first through multi-methodological comparative analyses applied to geodetic, hydrological and soil-properties patterns, then through the mapping of the river zones prone to failure. Since the input consists of time series satellite-derived data, the geospatial Artificial Intelligence is applied for extracting knowledge from spatial big data and for increasing the performance of data computing. In particular, machine learning is initially developed for selecting the relevant geographical areas (i.e. rivers, levees and riverbanks) from large geo-referential datasets. Then, since the spatial-distributed points are also time-dependent, the trends of different datasets are compared point by point by selected analytical techniques. Finally, in accordance with the acquired knowledge from previous steps, the system extracts information on the correlation indexes in order to make sense of patterns in space and time and to identify hierarchic orders for the realization of hazard maps. The proposed method is “wide” because, unlike other direct surveys, it is able to monitor large spatial areas since it is based on satellite-derived data. It is also “fast” because it is based on the Earth’s surface observation and is not connected with Earth’s inland investigations (such as the geotechnical and geophysical ones) or with forecasting models (e.g. hydraulic and flooding simulations). Due to these peculiarities, the method can support flood protection studies and can be used for driving the localization of river portions prone to failure, where focusing detailed geotechnical and geophysical surveys.</p>