Search Results (51)

The analysis of earthquake source mechanisms is key for seismotectonic studies, but it is often limited to traditional methods plagued with issues of precision and automation. This is particularly true in low-seismicity areas with deep and/or hidden seismogenic sources, where the identification of precise source mechanisms is a difficult and non-trivial task. In this study, we present a detailed application of TESLA (Tool for automatic Earthquake low-frequency Spectral Level estimAtion), a novel tool designed to overcome these limitations. We demonstrated TESLA’s effectiveness in defining source mechanism analysis by applying it to seismic sequences that occurred near Asti (AT), in the Monferrato area (Southern Piedmont, Italy). Our analysis reveals that the observed clusters consist of two distinct seismic sequences, occurring in 1991 and 2012, which were activated by the same seismogenic source. We relocated a total of 36 events with magnitudes ranging from 1.1 to 3.7, using a 3D velocity model, and computed 12 well-constrained focal mechanism solutions using the first motion polarities and the low-frequency spectral level ratios. The results highlight a relatively small seismogenic source located at approximately 5 km north of Asti (AT), at a depth of between 10 and 25 km, trending SW–NE with strike-slip kinematics. A smaller cluster of three events shows an activation of a different fault segment at around 60 km of depth, also showing strike-slip kinematics. These findings are in good agreement with the regional stress field acting in the Monferrato area and support the use of investigation tools such as TESLA for microseismicity analysis. Full article

Recent advancements in synchrophasor measurement technologies have introduced an unprecedented level of visibility in power distribution networks (PDNs), providing a high-quality data foundation for the accurate perception of event source locations. However, the high cost and deployment expense pose a significant challenge in balancing system observability and event source location identification (ESLI) accuracy. In this paper, we propose a staged ESLI scheme based on voltage measurement deviation (VMD), which can achieve high-precision ESLI and event current calculations under extremely low-observability conditions, where the measurement devices are deployed only at the head substation and terminal buses. By setting an unknown event injection current and traversing each bus along the target feeder to derive the terminal bus voltage and its outgoing current, an ESLI model based on virtual event current injection (VCI) is constructed, which not only assists in the ESLI task but also confers the solving capability of the event current. Leveraging the event current calculation ability of the ESLI model, a VMD-based staged ESLI algorithm is developed, achieving an ordered and accurate search for the exact location of the event source in a goal-oriented manner. The effectiveness of the developed ESLI algorithm is evaluated on the IEEE 33-bus test system. Experimental results demonstrate that our VMD achieves high-precision ESLI and event current solving in PDNs under extremely low observability, significantly outperforming the state-of-the-art ESLI methods. Full article

The NW–SE-trending Firenze-Pistoia Basin (FPB) is an intermontane tectonic depression in the Northern Apennines (Italy) bounded to the northeast by a SW-dipping normal fault system. Although it has moderate historical seismicity (maximum estimated Mw 5.5 in 1895), the FPB lacks detailed characterization of its recent tectonic structures, unlike those of nearby basins that have produced Mw > 6 events. This study focuses on the southeastern sector of the basin, including the urban area of Florence, using tectonic geomorphology derived from remote sensing, in particular LiDAR data, field verification, and high-resolution geophysical surveys such as electrical resistivity tomography and seismic reflection profiles. The integration of these techniques enabled interpretation of the subdued and anthropogenically masked tectonic structures, allowing the identification of Holocene activity and significant, although limited, surface vertical offset for three NE–SW-striking normal faults, the Peretola, Scandicci, and Maiano faults. The Scandicci and Maiano faults appear to segment the southeasternmost strand of the master fault of the FPB, the Fiesole Fault, which now shows activity only along isolated segments and cannot be considered a continuous active fault. From empirical relationships, the Scandicci Fault, the most relevant among the three active faults, ~9 km long within the basin and with an approximate Late Quaternary slip rate of ~0.2 mm/year, might source Mw > 5.5 earthquakes. These findings highlight the need to reassess the local seismic hazard for more informed urban planning and for better preservation of the cultural and architectural heritage of Florence and the other artistic towns located in the FPB. Full article

Reliable identification of river hydrograph separation is crucial for prioritizing water source areas to be protected from pollution. A field study was carried out in a hilly catchment with diverse land uses, located in Southwest China. A novel water-tracing method, combining the ratio of two conservative fluorescent components of dissolved organic matter, two ion ratios, and oxygen-18, was proposed for river hydrograph separation with MixSIAR. During a rain event with the longest preceding no-rain period, a set of four tracers were found to be applicable to drainage areas with diverse land uses. Notably, a drier antecedent soil moisture condition could favor the occurrence of more tracers qualified for distinguishing multiple water sources of river flow. Full article

The Oder River, situated along the border between Poland and Germany, is regularly affected by ice-jam events and their associated hazards, such as a sudden rise in water level and the endangerment to flood-protection infrastructure. The existing databases on past ice-jam events lack substantial information considering ice formation, blockage origins or the spatiotemporal evolution of the ice cover needed for a comprehensive understanding of relevant ice processes. Within this study, the evaluation of satellite and Uncrewed Aerial Vehicle (UAV) data was carried out in order to analyze the capabilities of enhancing river ice information in the study area. Satellite imagery was proven to be a valuable source of investigating ice-jam phenomena on all scales, leading to the identification of initial ice-jam locations, surveying spatiotemporal ice cover evolution or monitoring the maximum ice-cover extent. A simplified approach for river ice classification of satellite radar data using the K-Means Cluster Analysis is introduced, enabling the differentiation between river ice formations. Based on UAV data taken in this study, workflows were presented, allowing for measurements of ice floe velocities and the localization of flooded and ice-covered flow control structures. Full article

The partial discharge (PD) event in high-voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of the acoustic pulse and the acoustic reflections pose fundamental challenges to the acoustic emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse’s shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate the reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system. Full article

Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions. Full article

Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km²) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon. Full article

This paper reports on the first daily global monitoring program for natural gas flaring and industrial sites producing waste heat based on satellite observed infrared emissions. The Visible Infrared Imaging Radiometer Suite (VIIRS) collects nightly global infrared data in spectral bands ranging from near infrared (NIR) to longwave infrared (LWIR), providing a unique capability to detect and characterize infrared emitters at night. The VIIRS nightfire (VNF) algorithm identifies infrared (IR) emitters with multiple spectral bands and calculates the temperature, source area, and radiant heat via Planck curve fitting and physical laws. VNF data are produced nightly and extend from 2012 to the present. The most common infrared emitter is biomass burning, which must be filtered out. Industrial IR emitters can be distinguished from biomass burning based on temperature and persistence. The initial filtering to remove biomass burning was performed with 15 arc second grids formed from eleven years of VIIRS data, spanning 2012–2022. The locations and shapes of the remaining features were used to guide the generation of super-resolution pixel center clouds. These data clouds were then analyzed to define bounding vectors for single emitters and to split larger clusters into multiple emitters. A total of nearly 20,000 IR emitters were identified; each was assigned an identification number, and the type of emitter was recorded. Nightly temporal profiles were produced for each site, revealing activity patterns back to 2012. Nightly temporal profiles were kept current with weekly updates. Temporal profiles from individual sites were aggregated by country to form monthly profiles extending back to 2012. The nightly and monthly temporal profiles were suitable for analyzing industrial production, identifying disruption events, and tracking recovery. The data could also be used in tracking progress in energy conservation and greenhouse gas emission inventories. Full article

The use of X-rays for non-invasive imaging has a long history, which has resulted in several well-established methods in preclinical as well as clinical applications, such as tomographic imaging or computed tomography. While projection radiography provides anatomical information, X-ray fluorescence analysis allows quantitative mapping of different elements in samples of interest. Typical applications so far comprise the identification and quantification of different elements and are mostly located in material sciences, archeology and environmental sciences, whereas the use of the technique in life sciences has been strongly limited by intrinsic spectral background issues arising in larger objects, so far. This background arises from multiple Compton-scattering events in the objects of interest and strongly limits the achievable minimum detectable marker concentrations. Here, we review the history and report on the recent promising developments of X-ray fluorescence imaging (XFI) in preclinical applications, and provide an outlook on the clinical translation of the technique, which can be realized by reducing the above-mentioned intrinsic background with dedicated algorithms and by novel X-ray sources. Full article

This limited-scope study demonstrates the application of probabilistic risk assessment (PRA) methodologies to a spent fuel storage system for spent pebble-filled dry cask with a focus only on the necessary PRA technical elements sufficient to risk-inform the spent fuel storage system design. A dropping canister scenario in a silo of the spent fuel storage system is analyzed through an initiating event (IE) identification from the Master Logic Diagram (MLD); event sequence analysis (ES) by establishing the event tree; data analysis (DA) for event sequence quantification (ESQ) with uncertainty quantification; mechanistic source term (MST) analysis by using ORIGEN; radiological consequence analysis (RC) by deploying MicroShield, and risk integration (RI) by showing the Frequency-Consequence (F-C) target curve in the emergency area boundary (EAB). Additionally, a sensitivity study is conducted using the ordinary least square (OLS) regression method to assess the impact of variables such as failed pebble numbers, their location in the canister, and building wall thickness. Furthermore, the release categories grouped from the end states in the event tree are verified as safety cases through the F-C curve. This study highlights the implementation of PRA elements in a logical and structured manner, using appropriate methodologies and computational tools, thereby showing how to risk-inform the design of a dry cask system for storing spent pebble-filled fuel. Full article

Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the system’s situational awareness, the grid dynamics of the entire DER integration processes must be carefully monitored using synchronized high-resolution real-time measurement data from physical devices installed in the DN. μPMUs have been introduced into the DN to help with this. In comparison to traditional measurement devices, μPMUs can measure voltage, current, and their phasors, in addition to frequency and rate of frequency change (ROCOF). In this study, an approach to generating realistic event data for a real utility DN utilizing strategically installed μPMUs is proposed. The method employs an IEEE 34 test feeder with 12 μPMUs installed in strategic locations to generate real-time events-based realistic μPMU data for various situational awareness applications in an unbalanced DN. The node voltages and line currents were used to analyze the various no-fault and fault events. The author generated the data as part of his PhD research project, utilizing his real-time utility grid operation experience to be used for various situational awareness and fault location studies in a real unbalanced DN. The DN was modeled in DIgSILENT PowerFactory (DP) software. The generated realistic μPMU data can be utilized for developing data-driven algorithms for different event-detection, classification and section-identification research works. Full article

The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time. Full article

The main objective of the present study is the development of a comprehensive methodology for the estimation of the Carbon Footprint (CF) of research project activities and the identification of the best practices that can be followed by project partners within the project implementation to reduce its carbon footprint. The CF methodology is based on the GHG Protocol Guidance and the emissions factors of the Department for Environment Food & Rural Affairs (DEFRA). The emissions sources related to project activities are the following: heating (from fuels combustion), electricity, water, work-commuting, materials, printable deliverables, IT equipment and events. An application study is performed for a research project focusing on the Mediterranean area and it is found that on-site events represent a 41% share of the total CF of the project. The use of public transport and soft mobility by employees can result in a −37% reduction in the CF of work-commuting. The most significant best practices for more sustainable organization of project events, leading to a reduction of −62% and −50% in the CF of the events, are (1) public transportation and soft mobility of the events’ participants to reach the event location within the host city, and (2) the promotion of the use of buses and railway for the international/national travels of participants to/from the event’s host city, respectively. Τhe organization of hybrid events may also reduce the project event’s CF by −50%. The cumulative reduction in the total CF of the project examined from all the CF mitigation scenarios studied, relevant to the energy-efficient target of the EU, the origin of materials used, work-commuting and events (materials used, transportation, hybrid events), is estimated to be −45%. Full article

Harmful Algal Blooms (HABs) have been classified depending on the causative organism and its impacts: non-toxic HAB (microalgae capable of affecting tourism and causing oxygen deficiency, which generates mortality of marine organisms), toxic HAB (microalgae capable of transferring toxins to the food chain), and ichthyotoxic HAB (microalgae capable of generating mechanical damage in fish). HABs represent a worldwide problem and have apparently increased in frequency, intensity, and geographic distribution at different latitudes. This review details the occurrence of HAB events in the Southeast Pacific, Chile, over a 65-year period, analysing two of the three types of HAB described: toxic and ichthyotoxic HABs. For this, we conducted a review from many different scientific sources and from the written press and social media, that have mentioned HAB events in the country. In Chile, the microalgae involved in HAB events are dinoflagellate (52%), diatoms (33%) and silicoflagellate (10%), with a total of 41 species and/or genera described in the literature. A total of 501 HAB events were recorded in Chile between 1956 and 2021, where 240 (47.9%), 238 (47.5%), 14 (2.7%), 8 (1.5%) and 1 (0.2%) event were caused by diatoms, dinoflagellate, silicoflagellate, raphidophycean and haptophyte, respectively. An apparent increase in the frequency of HAB events is observed since the first record in 1956, with a maximum of 46 events during the years 2017 and 2019. The highest incidence in fish is caused by the group of silicoflagellate, raphidophycean and haptophyte (23 events), where 10 events caused mortalities in salmon with an incidence rate of 43.4%. Unlike what is observed with diatoms and dinoflagellate, the events associated with these groups are less frequent, but hold a much higher salmon mortality rate. During the last 65 years, HAB’s geographic extent shows an apparent trend to increase south-to-north. However, the identification of events is closely linked to the areas where much of the country’s aquaculture is located and, therefore, it could be biased. In turn, it is observed that the apparent increase in HAB events could be associated with a greater monitoring effort after major events (e.g., after the 2016 HAB event). On the other hand, it is also recognized a lack of knowledge about harmful algae throughout the Chilean Humboldt Current system, particularly in the northern regions, such as Atacama and Coquimbo. Therefore, the total number of blooms that have occurred in fjords and channels, particularly those that have caused minor economic impacts for artisanal fishermen and the salmon and mussel farming sector, might be underestimated. Full article