<p>The regionalized knowledge of the quality of near-surface rock masses is an important tool for land management/planning, as well as for guiding further in-depth studies aimed at landslide and earthquake risk assessment and civil engineering planning. The characterization of heterogeneous rock masses like flysch units represents a relevant challenge to engineering geologists due to the complex structure of these materials, which results from both their depositional context and tectonic history. Flysches are widespread all over the Apennines chain and their mechanical characterization is a difficult task given the occurrence of intercalation of layers with different lithology and strength. Moreover, the complexity of the thrust and fold tectonic framework makes the regional distribution of these characters difficult to predict. The aim of this work is to provide a method to map the near-surface rock masses quality for an arenaceous flysch widely cropping out in the outer Northern Apennines (Torrente Carigiola Formation, Aquitanian; Bettelli et al., 2002). This formation is mapped in both the geological map of the Regione Toscana (Italy) at the scale of 1:10,000 and the geological sheet “252 – Barberino di Mugello” (Bettelli et al., 2002) of the Italian Geological Map at the scale of 1:50,000 (CARG). It is made up by intercalated arenaceous (A) and pelitic (P) layers characterized by variable A/P ratio. The rock mass quality is evaluated by estimating, for a set of representative rock outcrops, the Rock Mass Quality Index (RQI; Disperati et al. 2016; Mammoliti et al. 2018). This index results from the analysis of both systematic Schmidt hammer rebound measurements (R) acquired at the nodes of a regular grid (ca. 20 R measurements for ca. 15-25 nodes) and the determination of the unit weight for representative outcrop rock samples. For the same outcrops, also the A/P ratio and bedding attitude are determined. The results show a positive linear correlation between RQI and the A/P ratio, confirming that the latter parameter is an important feature controlling the rock mass strength. This correlation is used to assess the distribution of both parameters within a set of geological cross sections traced normal to the regional structures trend (main thrusts and km-scale folds). Then, the structural features available from the literature geological maps allow us to extrapolate both the RQI and A/P ratio from the profiles to the map scale. Finally, a further set of the same rock outcrop data acquired after the above-described modelling procedure is used to check the accuracy of the method.</p>

<p>The knowledge of rock masses behaviour is an important information in various fields such as civil engineering, land use planning and hazard/risk zoning. Different rock mass classification methods, initially aimed at assisting underground excavations (Hoek, 2007), are widely used nowadays for preliminary design procedures (Bieniawski, 1989; Hoek, 2007), like the RMR (Bieniawski, 1976) and the Q (Barton et al., 1974) and their modifications. These methods incorporate geological, geomechanical and geometric parameters in order to obtain a quantitative estimation of the rock mass quality, but, on the other hand, their implementation is time-consuming. Despite the dominance of these two methods, further rock mass classifications systems have been proposed in the last decades and, among these, the Geological Strength Index (GSI) classification system is currently widely used as it allows to estimate the strength of rock mass through empirical semi-quantitative evaluation (Hoek, 1994; Cai et al., 2004), based on both rock mass structure and condition of the joints (Hoek et al., 1995). Estimating the GSI is straightforward and fast, but it comes at the cost of a certain degree of subjectivity. Moreover, the index does not adequately account for the lithology of the rock mass matrix. Hence, for the above reasons, these classification methods are not fully suitable to collect rock mass data over wide scale areas for engineering geological mapping. The Rock mass Quality Index (RQI, Disperati et al., 2016; Mammoliti et al., 2018) is a rock mass classification system developed for cartographic purposes and it is based on the systematic fieldwork measurement and processing of sets of the Schmidt hammer rebound values (R). Each representative rock mass outcrop is analysed by collecting ca. 20 R values at the 15-25 nodes of a regular grid conceived to investigate the typical features of the rock mass. This allows to perform statistical analyses and to calculate the RQI, a quantitative indicator of the global strength and quality of the rock mass. In the last decade, a dataset of ca 1100 outcrops sites spreading over a large area (ca. 12000 km<sup>2</sup>) were acquired in Tuscany (Italy), according to different lithology, weathering, jointing conditions. The dataset consists of both RQI measurements and GSI estimations for the main different lithological groups (flysch, limestones, marls, magmatic rocks and schists) of the Northern Apennines (Italy), as well as the laboratory determinations of the Slake Durability Index (Id2; Franklin & Chandra, 1974) obtained by testing representative outcrop rock samples. The large dataset has allowed to analyse the correlation among RQI, GSI and Id2 and to perform an in-depth critical analysis of the relationships among RQI, lithology, rock mass structure, as well as the suitability of the RQI as reference index for engineering geological mapping of near-surface rock mass quality.</p>

<p>Landslides are considered one of the major hazards causing economic and human losses worldwide. Slope instability processes are affecting buildings and infrastructures in the towns of the eastern slope of the Mt. Amiata volcanic complex (Tuscany, Italy). These processes are relevant as they expose the inhabitants to risk, moreover their analysis provide hints about the mechanisms and roles of land sliding in the progressive disruption of extinct volcanic edifices.</p><p>In this study we present the first results of some monitoring and multi-temporal systems which are integrated to investigate the spatial-temporal ground displacement field in the eastern slope of the Mt. Amiata volcanic complex. In detail, we combine InSAR, GNSS, robotic total stations (TS) and levelling techniques to obtain a framework in terms of planimetric and vertical displacements. We apply the Multi-Temporal InSAR approach from 2014 to 2021 using the ESA Copernicus Sentinel-1 data. To perform the interferometry analysis, we implement the single master Stanford Method for Persistent Scatterers (StaMPS) approach for both ascending and descending geometries, and by combining both Line of Sight (LOS) results, we reveal the vertical and E-W components of the displacement. In addition, we perform multi-temporal survey-style GNSS measurements for some tens stations from 2019 to present day. About one hundred reflectors are continuously monitored by TS. Additionally, multi-temporal geometric levelling is performed to assess the vertical movements of selected relevant benchmarks. Finally, results from different monitoring systems are combined to model the ground displacements.</p><p>The InSAR results reveal mean velocity vectors with standard deviation less than 1 mm/y. The GNSS results have higher signal to noise ratio in the horizontal components with residuals lower than 10 mm. Accuracies of the geometrical levelling and TS results are ca. 1 mm and ca. 5 mm respectively. By combining the results, the magnitude of displacement field is ranging up to ca. 30 cm/y. The different systems provide results each other reasonably coherent in terms of magnitude and direction of the displacement vector. Integration of systems allows us to get solutions where one or more systems fail to provide data (i.e., when few or no PS are obtained by InSAR). Finally, we compare the results with seasonal data like rainfall. Velocities tend to reduce during summer low precipitation periods, while they increase during winter. Long term quantitative monitoring activities will allow us to better understand the spatial-temporal evolution of the landslide processes in the perspective of developing an early warning system.</p>

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