Marsella, Maria

Due to its unique geophysical features and to the low density population of the area, Sos Enattos is a promising candidate site to host the Einstein Telescope (ET), the third-generation Gravitational Wave Observatory. The geophysical characterization of the Sos Enattos former mine, close to one of the proposed ET corners, started in 2010 with the deployment of seismic and environmental sensors underground. Since 2019 a new extensive array of seismometers, magnetometers and acoustic sensors have been installed in three stations along the underground tunnels, with one additional station at the surface. Beside a new geological survey over a wider area, two boreholes about 270 m deep each were excavated at the other two corners, determining the good quality of the drilled granite and orthogneiss rocks and the absence of significant thoroughgoing fault zones. These boreholes are instrumented with broadband seismometers that revealed an outstanding low level of vibrational noise in the low-frequency band of ET-LF (2-10Hz), significantly lower than the Peterson's NLNM and resulting among the quietest seismic stations in the world in that frequency band. The low seismic background and the reduced number of seismic glitches ensure that just a moderated Newtonian noise subtraction would be needed to achieve the ET target sensitivity. Geoelectrical and active seismic campaigns have been carried out to reveal the features of the subsurface, revealing the presence of small-sized fractured areas with limited water circulation. Finally, temporary arrays of seismometers, magnetometers and acoustic sensors are deployed in the area to study the local sources of environmental noise.

In the last decade, video surveillance cameras have experienced a great technological advance, making capturing and processing of digital images and videos more reliable in many fields of application. Hence, video-camera-based systems appear as one of the techniques most widely used in the world for monitoring volcanoes, providing a low cost and handy tool in emergency phases, although the processing of large data volumes from continuous acquisition still represents a challenge. To make these systems more effective in cases of emergency, each pixel of the acquired images must be assigned to class labels to categorise them and to locate and segment the observable eruptive activity. This paper is focused on the detection and segmentation of volcanic ash plumes using convolutional neural networks. Two well-established architectures, the segNet and the U-Net, have been used for the processing of in situ images to validate their usability in the field of volcanology. The dataset fed into the two CNN models was acquired from in situ visible video cameras from a ground-based network (Etna_NETVIS) located on Mount Etna (Italy) during the eruptive episode of 24th December 2018, when 560 images were captured from three different stations: CATANIA-CUAD, BRONTE, and Mt. CAGLIATO. In the preprocessing phase, data labelling for computer vision was used, adding one meaningful and informative label to provide eruptive context and the appropriate input for the training of the machine-learning neural network. Methods presented in this work offer a generalised toolset for volcano monitoring to detect, segment, and track ash plume emissions. The automatic detection of plumes helps to significantly reduce the storage of useless data, starting to register and save eruptive events at the time of unrest when a volcano leaves the rest status, and the semantic segmentation allows volcanic plumes to be tracked automatically and allows geometric parameters to be calculated.

Third-generation gravitational wave observatories will extend the lower frequency limit of the observation band toward 2 Hz, where new sources of gravitational waves, in particular intermediate-mass black holes (IMBH), will be detected. In this frequency region, seismic noise will play an important role, mainly through the so-called Newtonian noise, i.e., the gravity-mediated coupling between ground motion and test mass displacements. The signal lifetime of such sources in the detector is of the order of tens of seconds. In order to determine whether a candidate site to host the Einstein Telescope observatory is particularly suitable to observe such sources, it is necessary to estimate the probability distributions that, in the characteristic time scale of the signal, the sensitivity of the detector is not perturbed by Newtonian noise. In this paper, a first analysis is presented, focused on the Sos Enattos site (Sardinia, Italy), a candidate to host the Einstein Telescope. Starting from a long data set of seismic noise, this distribution is evaluated considering both the presently designed triangular ET configuration and also the classical ”L” configuration.

Only a few high precision studies of lava and tephra during simultaneous explosive and effusive activity have so far been undertaken. We carried out such measurements by analysis of a unique and homogeneous multi-temporal dataset of highspatial resolution satellite optical images. Digital Elevation Models (DEMs) and orthophotos (with 1- and 0.5-m-pixel resolutions respectively) were extracted from six specifically tasked Pleiades tri-stereo pairs of Mt. Etna volcano, between 2011 and 2016. During this period, frequent effusive and explosive events formed lava flow fields and built up the new south-east crater pyroclastic cone. The volumes of lava fields and proximal pyroclastic deposits were measured by comparing the Pleiades DEMs with an aerial photogrammetric DEM updated in 2007. The volumes of all distal deposits were estimated using lava and tephra partitioning from the literature for an Etnean lava fountain. The dense rock equivalent volume of lava and tephra, calculated to be 248.4 ± 2.1 × 106 m3 in total, corresponds to an average output rate of 0.98 m3/s over the analysed 8-year period (May 2008–May 2016) and to a multi-event eruption rate of 5.53 m3/s for 520 days of activity. The multi-temporal analysis of high-spatial resolution satellite DEMs, here successfully applied to the well-monitored Etna volcano, demonstrated that the tasking of high-spatial resolution satellite images is crucial for fast and effective monitoring during intense volcanic activity (frequent and overlapping eruptive events). This methodology could be used for the monitoring of remote or hazardous volcanoes that are difficult to study by means of repeated field surveys.

In areas prone to landslides, the identification of potentially unstable zones has a decisive impact on the risk assessment and development of mitigation plans. Active volcanic islands are particularly prone to instability phenomena as they are always in the early stage of dynamic unrest. A historical example of slope instability is the landslide which occurred in 1988 along the northwestern flank of La Fossa Cone on the island of Vulcano (Aeolian Archipelago). Based on this past activity, a susceptibility assessment using the bivariate technique of the GIS matrix method (GMM) was carried out on the islands of Lipari and Vulcano. Nevertheless, this case is congruent with those where a part of the surface was not assigned to stable or unstable areas, since a comprehensive inventory was only available for the island of Lipari. Some of the implemented steps of the susceptibility matrix method were modified to enable the model developed in the Lipari area to be applied to both islands. Considering the important role that the classification of conditioning factors plays in susceptibility analysis, the degree of association with landslide spatial distribution for the multiple classifications of each factor was assessed. Furthermore, an innovative clustering approach based on text and data mining techniques (self-organizing map neural network) was applied and compared with a heuristic classification of the categorical variable of lithology units. In addition to the extensive contingency analysis, up to 14 factor combinations were submitted to the GMM, validated and compared so as to select the one that best explains the susceptibility zoning. The effects of these incorporated processes in the previous phase of classification were discussed and preliminary susceptibility map was generated for both islands. After the validation of the susceptibility assessment, it is shown that the highest classes (High and Very High) matched 76.9% (relative accuracy) of the test inventory, while the lower susceptibility classes (Very Low and Low) resulted in a degree of fit of 14.39% (relative error).

In order to improve the observation capability in one of the most active volcanic areas in the world, Mt. Etna, we developed a processing method to use the surveillance cameras for a quasi real-time mapping of syn-eruptive processes. Following an evaluation of the current performance of the Etna permanent ground NEtwork of Thermal and Visible Sensors (Etna_NETVIS), its possible implementation and optimization was investigated to determine the locations of additional observation sites to be rapidly set up during emergencies. A tool was then devised to process time series of ground-acquired images and extract a coherent multi-temporal dataset of georeferenced map. The processed datasets can be used to extract 2D features such as evolution maps of active lava flows. The tool was validated on ad-hoc test fields and then adopted to map the evolution of two recent lava flows. The achievable accuracy (about three times the original pixel size) and the short processing time makes the tool suitable for rapidly assessing lava flow evolutions, especially in the case of recurrent eruptions, such as those of the 2011–2015 Etna activity. The tool can be used both in standard monitoring activities and during emergency phases (eventually improving the present network with additional mobile stations) when it is mandatory to carry out a quasi-real-time mapping to support civil protection actions. The developed tool could be integrated in the control room of the Osservatorio Etneo, thus enabling the Etna_NETVIS for mapping purposes and not only for video surveillance.

The ground monitoring of an active volcanic area presents many complexities. By exploiting the remote sensing techniques, we developed an analytical methodology for observing and quantifying eruptive processes and the related phenomena (lava flows, volcanic avalanche/landslides, slope stability features). This methodology integrates HR optical images and SAR interferometry, acquired in different time frames and was tested on the case study of Mount Etna. The extraction of new cartographic products allows us to define the volcanic hazards that may impact on the surrounding populated areas and infrastructures.

In order to improve the observation capability in one of the most active volcanic areas in the world, Mt. Etna, we developed a processing method to use the surveillance cameras for a quasi real-time mapping of syn-eruptive processes. Following an evaluation of the current performance of the Etna permanent ground NEtwork of Thermal and Visible Sensors (Etna_NETVIS), its possible implementation and optimization was investigated to determine the locations of additional observation sites to be rapidly set up during emergencies. A tool was then devised to process time series of ground-acquired images and extract a coherent multi-temporal dataset of georeferenced map. The processed datasets can be used to extract 2D features such as evolution maps of active lava flows. The tool was validated on ad-hoc test fields and then adopted to map the evolution of two recent lava flows. The achievable accuracy (about three times the original pixel size) and the short processing time makes the tool suitable for rapidly assessing lava flow evolutions, especially in the case of recurrent eruptions, such as those of the 2011–2015 Etna activity. The tool can be used both in standard monitoring activities and during emergency phases (eventually improving the present network with additional mobile stations) when it is mandatory to carry out a quasi-real-time mapping to support civil protection actions. The developed tool could be integrated in the control room of the Osservatorio Etneo, thus enabling the Etna_NETVIS for mapping purposes and not only for video surveillance.

In active volcanic areas it is often difficult carry out direct surveys during an eruption, remote sensing techniques based on airborne/satellite platforms and ground-based sensors have remarkable monitoring potentialities in terms of safety and observation capability. In addition, the recent development of high resolution digital cameras, laser scanners and SAR instruments have improved the ability to obtain reliable measurements for modelling the evolution of effusive and explosive eruptions by following the rate of advancement of a lava flow or the dispersal of a volcanic plume. In order to collect data at an adequate level of accuracy and frequency it is not possible to exclusively rely on airborne or satellite methods and it is necessary to carry out measurements using also remote sensing instruments operating on the ground. Among the other techniques, the use of a simplified photogrammetric approach based a video-surveillance camera network represents a straightforward alternative for rapid mapping in active volcanic areas. Therefore a procedure for optimizing and extending the observational capability of the Etna NEtwork of Thermal and VIsible cameras (NETVIS) for systematically monitoring and quantifying surface sin-eruptive processes was implemented. The activity included also the extension of the permanent video-surveillance network by installing additional mobile stations. A dedicated tool for automatic processing of image datasets was developed and tested in both simulated and real scenarios to obtain a time series of digital orthophotos for tracking the evolution of a lava flow emplacement. The developed tool was tested by processing images acquired by the Etna_NETVIS sensors, in particular from Monte Cagliato thermal camera, during the 2011 paroxysmal episodes of the New South East Crater that poured lava flows in the Valle del Bove.

In volcanic areas, where it can be difficult to perform direct surveys, digital photogrammetry techniques are rarely adopted for routine volcano monitoring. Nevertheless, they have remarkable potentialities for observing active volcanic features (e.g., fissures, lava flows) and the connected deformation processes. The ability to obtain accurate quantitative data of definite accuracy in short time spans makes digital photogrammetry a suitable method for controlling the evolution of rapidly changing large-area volcanic phenomena. The systematic acquisition of airborne photogrammetric datasets can be adopted for implementing a more effective procedure aimed at long-term volcano monitoring and hazard assessment. In addition, during the volcanic crisis, the frequent acquisition of oblique digital images from helicopter allows for quasi-real-time monitoring to support mitigation actions by civil protection. These images are commonly used to update existing maps through a photo-interpretation approach that provide data of unknown accuracy. This work presents a scientific tool (Orthoview) that implements a straightforward photogrammetric approach to generate digital orthophotos from single-view oblique images provided that at least four Ground Control Points (GCP) and current Digital Elevation Models (DEM) are available. The influence of the view geometry, of sparse and not-signalized GCP and DEM inaccuracies is analyzed for evaluating the performance of the developed tool in comparison with other remote sensing techniques. Results obtained with datasets from Etna and Stromboli volcanoes demonstrate that 2D features measured on the produced orthophotos can reach sub-meter-level accuracy.