Chiappini, Massimo

Detecting and preventing the illicit movement of radioactive materials within a country is crucial, requiring the identification of radiologic anomalies against the normal radiation background. High-purity germanium (HPGe) detectors, known for their precision and sensitivity, have become popular for analyzing radioactive materials in CBRNe scenarios. This study focused on calibrating an HPGe detector for CBRNe applications, using gamma-ray spectra from an IAEA-U reference source. Energy calibration involved identifying peaks in the spectra and creating a calibration curve for energy and channel number data. Efficiency calibration, determined using the known activity of the source, revealed a linear relationship between energy and detector response. Over four years, systematic efficiency calibrations showed a deviation of only 3% well below the recommended limit of 5%. These findings underscore the reliability of the system as a reference spectrometry method for accurate detection of radioactive materials.

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications. We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting. Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers. The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy, such as rotations, as well as to other types of measuring systems, such as magnetometers or gyroscopes.

Deformation across structural complexities such as along-strike fault bends may be accommodated by distributed faulting, with multiple fault splays working to transfer the deformation between two principal fault segments. In these contexts, an unsolved question is whether fault activity is equally distributed through time, with multiple fault splays recording the same earthquakes, or it is instead localized in time and space across the distributed faults, with earthquakes being clustered on specific fault splays. To answer this question, we studied the distributed deformation across a structural complexity of the Mt. Marine fault (Central Apennines, Italy), where multiple fault splays accommodate the deformation throughout the change in strike of the fault. Our multidisciplinary (remote sensing analysis, geomorphological-geological mapping, geophysical and paleoseismological surveys) study identified five principal synthetic and antithetic fault splays arranged over an across-strike distance of 500 m, all of which showing evidence of multiple surface-rupturing events during the Late Pleistocene-Holocene. The fault splays exhibit different and variable activity rates, suggesting that fault activity is localized on specific fault splays through space and time. Nonetheless, our results suggest that multiple fault splays can rupture simultaneously during large earthquakes. Our findings have strong implications on fault-based seismic hazard assessments, as they imply that data collected on one splay may not be representative of the behaviour of the entire fault. This can potentially bias seismic hazard calculations.

In this paper we present the new high-resolution magnetic anomaly map of the La Fossa Caldera system and Lipari island (Southern Italy), obtained by merging two low-altitude aeromagnetic surveys. In these islands a variegated ensemble of magnetic anomalies develops in the north-south direction. The La Fossa Caldera is characterized by very high-intensity and short-wavelength magnetic anomalies, related to mafic intrusive/effusive sources, mainly aligned along the NNW-SSE and NS faults. Instead, the Lipari island is characterized by lower intensity anomalies related to sources with more evolved chemistry, elongated in the NE-SW, NNE-SSW and EW of the subordinate faults. Both the two sets of structures belong to the Tindari-Letojanni strike slip fault-system, a regional lineament along which the southern side of the Aeolian Archipelago is emplaced. The study identifies three distinctive magnetic zones on the Lipari island: the southern, the central, and the north-western ones. The southern zone is characterized by negative magnetic monopoles in correspondence with the younger rhyolitic domes and a Curie Isotherm upwelling. The central part of the island has an average lower intensity of positive anomalies, corresponding to the less evolved products of the intermediate Lipari volcanic epochs. The north-western side is characterized by higher intensity anomalies related to older volcanic epochs. The interplay among strike-slip tectonics, chemistry of the uprising magmas, and the thermal setting has contributed to the overall anomaly pattern in this sector of the Aeolian Archipelago. This interpretation is supported by the application of digital enhancement to the total intensity magnetic anomaly field and by the spatial correlation analysis of the magnetic and volcano-tectonic features. The obtained insights are useful to better understand the relationship between arc volcanism and tectonics. Moreover, they can also outline new inferences to forecast future eruptions of the active La Fossa volcanic system. In fact, recent unrest signals were registered at La Fossa Cone, which is one of the present-day active volcanoes of the Aeolian Archipelago, along with Stromboli island.

Preprocessing is an essential task for the correct analysis of digital medical images. In particular, X-ray imaging might contain artifacts, low contrast, diffractions or intensity inhomogeneities. Recently, we have developed a procedure named PACE that is able to improve chest X-ray (CXR) images including the enforcement of clinical evaluation of pneumonia originated by COVID-19. At the clinical benchmark state of this tool, there have been found some peculiar conditions causing a reduction of details over large bright regions (as in ground-glass opacities and in pleural effusions in bedridden patients) and resulting in oversaturated areas. Here, we have significantly improved the overall performance of the original approach including the results in those specific cases by developing PACE2.0. It combines 2D image decomposition, non-local means denoising, gamma correction, and recursive algorithms to improve image quality. The tool has been evaluated using three metrics: contrast improvement index, information entropy, and effective measure of enhancement, resulting in an average increase of 35% in CII, 7.5% in ENT, 95.6% in EME and 13% in BRISQUE against original radiographies. Additionally, the enhanced images were fed to a pre-trained DenseNet-121 model for transfer learning, resulting in an increase in classification accuracy from 80 to 94% and recall from 89 to 97%, respectively. These improvements led to a potential enhancement of the interpretability of lesion detection in CXRs. PACE2.0 has the potential to become a valuable tool for clinical decision support and could help healthcare professionals detect pneumonia more accurately.

In order to constrain the Fault Displacement Hazard (FDH) of the town of Pizzoli, located 10 km NW of L’Aquila (Central Apennines, Italy), we performed two paleoseismological trenches across multiple fault splays within the hanging wall of the main Mt. Marine active normal fault. Our trenches highlighted the presence of five faults arranged both synthetic and antithetic to the main fault. The fault splays are distributed within an across-strike distance of about 500 m. Each fault segment shows evidence of repeated surface-rupturing earthquakes occurring throughout the Late Pleistocene-Holocene, proving their capability of rupturing the surface during recent earthquakes. Our study shows that multiple parallel fault splays belonging to a principal segmented fault are active during the same time interval, although the slip rates of single faults may be different through time. Our work reiterates the importance of performing paleoseismological investigation for assessing FDH in urban areas.

The InSEA project (“Initiatives in Supporting the consolidation and enhancement of the EMSO research infrastructure consortium (ERIC) and related Activities”) has the objective, as the full name of the project indicates, to consolidate and strengthen the infrastructures concerning the EMSO (“European Multidisciplinary Seafloor and water column Observatory”) ERIC (European Research Infrastructure Consortium) and all those technical-scientific activities related to it. In particular, the project is upgrading localized and distributed marine infrastructures, laboratories, observatories and spatial measurement activities in Southern Italian seas to support those activities of surveys in fixed time series points of observation of EMSO ERIC. The project is developing according to six implementation Objectives of Research (OR) that involve four National research Institutions: INGV, ISPRA, OGS and Anton Dohrn Zoological Station of Naples. The paper illustrates with more details the relevant objectives of the InSEA project and its most significant implementation phases.

Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy.

Rome Capital City is located in a high heat flux area of central Italy, suitable for low-enthalpy geothermal exploitation. In the central-northern part of the city, near Tor di Quinto hippodrome close to Tiber River, a wide undeveloped area occurs, which is a possible future urban development site. We present the results of a geochemical and geophysical study aimed at assessing the presence in this zone of a low-enthalpy geothermal aquifer and at evaluating its depth, thickness and the physico-chemical characteristics of the geothermal water. Furthermore the natural CO2 output of this zone has been investigated. A soil CO2 flux survey with 551 measurements over a surface of 3.09 km2 revealed the presence of parallel NW-SE trending positive flux anomalies. The total CO2 output was estimated to 87.77 t*day-1, most of which (85 %) of endogenous or mixed origin. An Electrical Resistivity Tomography survey, consisting of five parallel 355 m long and 100 m spaced profiles, allowed the reconstruction of the stratigraphy of the underground sediments, which are fluvial deposits of the near Tiber River. The geothermal water is hosted in a low-resistivity layer, corresponding to the Tiber base gravels, which are here 20 m thick and whose top is 40 m below the surface. The water has a nearly constant temperature of 17.5 °C, a relatively high salinity and an appreciable content in dissolved gas. This low-enthalpy resource is suitable for direct uses, e.g. individual and district heating/cooling, sanitary hot water, spa facilities for swimming and bathing.

Three-dimensional geological model of the area of the central Apennines affected by the 2016-2018 seismic sequence. The model consists of stratigraphic surfaces (top or basal unconformity of units, or sequences of units, with homogeneous behavior) and main faults of the area. The stratigraphic surfaces are, from the oldest to the most recent: the top of pre-Upper Triassic units, the top of the intra-Triassic units, the top of Calcare Massiccio, the top of the Marne