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De Angelis, Silvio
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De Angelis, Silvio
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- PublicationOpen AccessIntroduction to a community dataset from an infrasound array experiment at Mt. Etna, Italy(2021-09-23)
; ; ; ; ; ; ; Volcanic activity represents a hazard to population and infrastructure worldwide. The study of acoustic waves in the atmosphere by volcanic activity is growing in popularity as an effective tool to monitor and understand the mechanisms of eruptions. In 2019, we deployed two 6-element infrasound arrays at Mt. Etna, Italy, one of the most active volcanoes in the world. Our experiment captured a range of acoustic signals associated with diverse activity ranging from background degassing to energetic Strombolian explosions, lava flows, and atmospheric injection of volcanic ash. Here, we present a description of this valuable, publicly available, research dataset. We document the design and scope of the experiment, report on data availability, and present a brief summary of the activity observed at Mt. Etna during our deployment aiming to facilitate future use of these valuable data. This dataset is the first example of open data from a multiple infrasound array experiment at Mt. Etna and one of the few available globally.232 22 - PublicationOpen AccessRecurrent Scattering Network Detects Metastable Behavior in Polyphonic Seismo-Volcanic Signals for Volcano Eruption Forecasting(2022)
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; We introduce an end-to-end (E2E) deep neural network architecture designed to perform seismo-volcanic monitoring focused on detecting change. Due to the complexity of volcanic processes, this requires a polyphonic detection, segmentation, and classification approach. Through evolving epistemic uncertainty, invoking a Bayesian network strategy, we detect change and demonstrate its significance as an indicator for possible forecasting of eruptions using data from the Bezymianny and Etna volcanoes. Specifically, we propose morphing the scattering transform from previous work into a novel E2E hybrid and recurrent learnable deep scattering network to adapt to multi-scale temporal dependencies from streaming data. The time-dependent scattering is in some sense physics informed, namely, through time–frequency representation (TFR) of the data. At the same time, with a carefully designed deep convolutional LSTM (ConvLSTM) architecture, we learn intra-event, temporal dynamics from the scattering coefficients or features. We verify the effectiveness of transfer learning switching between volcanoes. Our experimental results set a new norm for semi-supervised seismo-volcanic monitoring.129 22 - PublicationOpen AccessVINEDA—Volcanic INfrasound Explosions Detector Algorithm(2019-12-13)
; ; ; ; ; ; ; ; ; ; ; ; ; Infrasound is an increasingly popular tool for volcano monitoring, providing insights of the unrest by detecting and characterizing acoustic waves produced by volcanic processes, such as explosions, degassing, rockfalls, and lahars. Efficient event detection from large infrasound databases gathered in volcanic settings relies on the availability of robust and automated workflows. While numerous triggering algorithms for event detection have been proposed in the past, they mostly focus on applications to seismological data. Analyses of acoustic infrasound for signal detection is often performed manually or by application of the traditional short-term average/long-term average (STA/LTA) algorithms, which have shown limitations when applied in volcanic environments, or more generally to signals with poor signal-to-noise ratios. Here, we present a new algorithm specifically designed for automated detection of volcanic explosions from acoustic infrasound data streams. The algorithm is based on the characterization of the shape of the explosion signals, their duration, and frequency content. The algorithm combines noise reduction techniques with automatic feature extraction in order to allow confident detection of signals affected by non-stationary noise. We have benchmarked the performances of the new detector by comparison with both the STA/LTA algorithm and human analysts, with encouraging results. In this manuscript, we present our algorithm and make its software implementation available to other potential users. This algorithm has potential to either be implemented in near real-time monitoring workflows or to catalog pre-existing databases.325 17 - PublicationOpen AccessMatlab Interface for Seismo‐Acoustic aRray Analysis (MISARA)(2024-04)
; ; ; ; ; ; ; ; ; Volcanic activity generates diverse seismic and acoustic signals that offer valuable insights into the underlying magmatic processes. Contemporary volcano monitoring relies on networks and arrays of seismic and acoustic sensors. The analysis of signals acquired by these instruments necessitates streamlined workflows and specialized software. The high sampling rates, typically exceeding 50 Hz, employed in recording seismic and acoustic waveforms by multi-station networks and dense arrays result in the swift accumulation of substantial data volumes, posing a formidable challenge in establishing efficient data analysis workflows for volcano surveillance. In this context, we introduce MISARA (Matlab Interface for Seismo-Acoustic aRray Analysis), an open-source MATLAB graphical user interface. MISARA is meticulously crafted to furnish a user-friendly workflow for analyzing seismo-acoustic data in volcanic settings. It incorporates efficient algorithmic implementations of established techniques for seismic and acoustic data analysis, with a focus on supporting the visualization, characterization, detection, and location of volcano seismo-acoustic signals. The intuitive and modular structure of MISARA facilitates swift, semi-automated data inspection and result interpretation, thereby minimizing user effort. Validation of MISARA involved testing it with seismo-acoustic data recorded at Etna Volcano (Italy) during 2010, 2011, and 2019. The tool is intended for educational and research purposes and is well-suited to aid routine data analysis at volcano observatories. Its open-source nature encourages collaborative development and adaptation, fostering advancements in volcano monitoring and contributing to the broader scientific community.12 1 - PublicationOpen AccessUncertainty in Detection of Volcanic Activity Using Infrasound Arrays: Examples From Mt. Etna, Italy(2020)
; ; ; ; ; ; ; ; ; ; ; ; ; The injection of gas and pyroclastic material from volcanic vents into the atmosphere is a prolific source of acoustic waves. Infrasound arrays offer efficient, cost-effective, and near real-time solutions to track the rate and intensity of surface activity at volcanoes. Here, we present a simple framework for the analysis of acoustic array data, based on least-squares beamforming, that allows to evaluate the direction and speed of propagation of acoustic waves between source and array. The algorithms include a new and computationally efficient approach for quantitative assessment of the uncertainty on array measurements based on error propagation theory. We apply the algorithms to new data collected by two 6-element infrasound arrays deployed at Mt. Etna during the period July–August 2019. Our results demonstrate that the use of two infrasound arrays allowed detecting and tracking acoustic sources from multiple craters and active vents associated with degassing and ash-rich explosions, vigorous and frequent Strombolian activity, opening of new eruptive fractures and emplacement of lava flows. Finally, we discuss the potential use of metrics based on infrasound array analyses to inform eruption monitoring operations and early warning at volcanoes characterized by episodic intensification of activity.260 14 - PublicationOpen AccessVolcanic Tremor Tracks Changes in Multi‐Vent Activity at Mt. Etna, Italy: Evidence From Analyses of Seismic Array Data(2022)
; ; ; ; ; ; ; ; ; ;; ;; Abstract The mild degassing and effusion that characterizes open-vent volcanoes can be interrupted by episodes of sustained explosive activity known as paroxysms. Here, we present observations from a seismic array deployment during the 2021 eruption of Mt. Etna, Italy. During the observation period, degassing dominated surface activity at the central and northeast summit craters; lava flows, Strombolian explosions, and fire fountaining, accompanied by ash plumes, characterized eruption in the southeast sector of Mt. Etna. Seismic array locations showed changes associated with shifts in the style and location of activity across multiple craters at Mt. Etna. We observed changes in array locations between the north-northeast and southeast directions that consistently anticipated the onset of paroxysmal activity in the southeast sector. Our results demonstrate the potential of seismic arrays to resolve vent-specific activity and shed light on precursory patterns leading up to paroxysmal activity.215 76 - PublicationOpen AccessRecent Developments and Applications of Acoustic Infrasound to Monitor Volcanic EmissionsVolcanic ash is a well-known hazard to population, infrastructure, and commercial and civil aviation. Early assessment of the parameters that control the development and evolution of volcanic plumes is crucial to effective risk mitigation. Acoustic infrasound is a ground-based remote sensing technique—increasingly popular in the past two decades—that allows rapid estimates of eruption source parameters, including fluid flow velocities and volume flow rates of erupted material. The rate at which material is ejected from volcanic vents during eruptions, is one of the main inputs into models of atmospheric ash transport used to dispatch aviation warnings during eruptive crises. During explosive activity at volcanoes, the injection of hot gas-laden pyroclasts into the atmosphere generates acoustic waves that are recorded at local, regional and global scale. Within the framework of linear acoustic theory, infrasound sources can be modelled as multipole series, and acoustic pressure waveforms can be inverted to obtain the time history of volume flow at the vent. Here, we review near-field (<10 km from the vent) linear acoustic wave theory and its applications to the assessment of eruption source parameters. We evaluate recent advances in volcano infrasound modelling and inversion, and comment on the advantages and current limitations of these methods. We review published case studies from different volcanoes and show applications to new data that provide a benchmark for future acoustic infrasound studies.
299 23 - PublicationOpen AccessVolume Flow Rate Estimation for Small Explosions at Mt. Etna, Italy, From Acoustic Waveform InversionRapid assessment of the volume and the rate at which gas and pyroclasts are injected into the atmosphere during volcanic explosions is key to effective eruption hazard mitigation. Here, we use data from a dense infrasound network deployed in 2017 on Mt. Etna, Italy, to estimate eruptive volume flow rates (VFRs) during small gas-and-ash explosions. We use a finite-difference time-domain approximation to compute the acoustic Green's functions and perform a full waveform inversion for a multipole source, combining monopole and horizontal dipole terms. The inversion produces realistic estimates of VFR, on the order of 4 × 104 m3/s and well-defined patterns of source directivity. This is the first application of acoustic waveform inversion at Mt. Etna. Our results demonstrate that acoustic waveform inversion is a mature and robust tool for assessment of source parameters and holds potential as a tool to provide rapid estimates of VFR in near real time.
311 16 - PublicationOpen AccessMISARA: Matlab Interface for Seismo-Acoustic aRray Analysis(2023)
; ; ; ; ; ; ; ; ; Volcanic activity produces a broad spectrum of seismic and acoustic signals whose characteristics provide important clues on the underlying magmatic processes. Networks and arrays of seismic and acoustic sensors are the backbone of most modern volcano monitoring programs. Investigation of the signals gathered by these instruments requires efficient workflows and specialist software. The high sampling rates, typically 50 Hz or greater, at which seismic and acoustic waveforms are recorded by multistation networks and dense arrays leads to the rapid accumulation of large volumes of data, making the implementation of efficient data analysis workflows for volcano surveillance a challenging task. Here, we present an open‐source MATLAB graphical user interface, MISARA (Matlab Interface for Seismo‐Acoustic aRray Analysis), designed to provide a user‐friendly workflow for the analysis of seismoacoustic data in volcanic environments. MISARA includes efficient algorithm implementations of well‐established techniques for seismic and acoustic data analysis. It is designed to support visualization, characterization, detection, and location of volcano seismoacoustic signals. Its intuitive, modular, structure facilitates rapid, semiautomated, inspection of data and results, thus reducing user effort. MISARA was tested using seismoacoustic data recorded at Etna Volcano (Italy) in 2010, 2011, and 2019, and is intended for use in education and research, and to support routine data analysis at volcano observatories.94 101 - PublicationOpen AccessBayesian Monitoring of Seismo-Volcanic DynamicsMethods for volcano monitoring that are based on analysis of geophysical data often rely on deterministic approaches without considering the complex and dynamic nature of volcanic systems. To detect subtle changes within seismic sequences associated with volcanic unrest, specialized workflows for data classification and analysis are required. Here, we present an inference framework based on Bayesian Deep Learning as a probabilistic proxy, which allows monitoring continuous changes in seismic activity at volcanoes. This architecture has been designed and trained to detect and to classify individual earthquake transients from continuous seismic data recorded in volcanic environments. We tested this new framework by analyzing seismic data associated with eruptions at Bezymianny Volcano (Russia) during 2007. Our results demonstrate efficient signal detection and classification accuracy, and effective detection of changes in the volcanic system in the hours preceding eruptive activity. This approach can be extended to other volcanoes and earthquake-prone areas, and demonstrates a new application of deep learning in the field of seismic monitoring.
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