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Introduction: Volcano seismology has successfully predicted several eruptions and includes many reliable methods that have been adopted extensively by volcanic observatories; however, there are several problems that still lack solutions. Meanwhile, the overwhelming success of data-driven models to solve predictive complex real-world problems positions them as an effective addition to the monitoring systems deployed in volcanological observatories. Methods: By applying signal processing techniques on seismic records, we extracted four different seismic features, which usually change their trend when the system is approaching an eruptive episode. We built a temporal matrix with these parameters then defined a label for each temporal moment according to the real state of the volcanic activity (Unrest, Pre-Eruptive, Eruptive). To solve the remaining problem developing early warning systems that are transferable between volcanoes, we applied our methodology to databases associated with different volcanic systems, including data from both explosive and effusive episodes, recorded at several volcanic scenarios with open and closed conduits: Mt. Etna, Bezymianny, Volcán de Colima, Mount St. Helens and Augustine. Results and Discussion: This work proposes the use of Neural Networks to classify the volcanic state of alert based on the behaviour of these features, providing a probability of having an eruption. This approach offers a Machine Learning tool for probabilistic short-term volcanic eruption forecasting, transferable to different volcanic systems. This innovative method classifies the state of volcanic hazard in near real-time and estimates a probability of the occurrence of an eruption, resulting in a period from at least hours to several days to forecast an eruption. [ABSTRACT FROM AUTHOR]

Real-time monitoring of volcano-seismic signals is complex. Typically, automatic systems are built by learning from large seismic catalogues, where each instance has a label indicating its source mechanism. However, building complete catalogues is difficult owing to the high cost of data-labelling. Current machine learning techniques have achieved great success in constructing predictive monitoring tools; however, catalogue-based learning can introduce bias into the system. Here, we show that while monitoring systems recognize almost 90 % of events annotated in seismic catalogues, other information describing volcanic behavior is not considered. We found that weakly supervised learning approaches have the remarkable capability of simultaneously identifying unannotated seismic traces in the catalogue and correcting mis-annotated seismic traces. When a system trained with a master dataset and catalogue is used as a pseudo-labeller within the framework of weakly supervised learning, information related to volcanic dynamics can be revealed and updated. Our results offer the potential for developing more sophisticated semi-supervised models to increase the reliability of monitoring tools. For example, the use of more sophisticated pseudo-labelling techniques involving data from several catalogues could be tested. Ultimately, there is potential to develop universal monitoring tools able to consider unforeseen temporal changes in monitored signals at any volcano. [ABSTRACT FROM AUTHOR]

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots -- defined here as localized volcanic thermal emissions -- in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of -3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (Fl-score 0.92) when evaluated on -1,700 unseen images from the same volcanoes, and 95% (Fl-score 0.67) when evaluated on -3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181-205), our model detects 22% more hotspots and produces l2% fewer false positives. Additional testing on -700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor's data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application. [ABSTRACT FROM AUTHOR]

In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed's neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out. [ABSTRACT FROM AUTHOR]

The search for pre‐eruptive observables that can be used for short‐term volcanic forecast remains a scientific challenge. Pre‐eruptive patterns in seismic data are usually identified by analyzing seismic catalogs (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcanic medium as a consequence of changes in the volume of the volcano. However, although successful volcanic predictions have been achieved, there is still no generally valid model suitable for a large range of eruptive scenarios. In this study, we evaluate the potential use of Shannon entropy as short‐term volcanic eruption forecasting extracted from seismic signals at five well studied volcanoes (Etna, Mount St. Helens, Kilauea, Augustine, and Bezymianny). We identified temporal patterns that can be monitored as short‐term eruptive precursors. We quantified the decay of Shannon entropy prior to eruptions, noting that changes appear between 4 days and 12 hr before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index), we can elaborate physical models according to the occurring volcanic processes. Our results show that pre‐eruptive variation in Shannon entropy is a confident short‐term volcanic eruption monitoring tool. Plain Language Summary: Volcanic eruptions represent a major natural hazard. Despite decades of research, the forecasting of volcanic eruptions remains a scientific challenge. Subsurface volcanic processes generate seismic waves, which can be measured at the surface using seismometers. To date, the most successful examples of eruption forecasting have been based on seismic data. However, we still lack a early warning model that can be applied across the wide range of eruption styles seen around the world. In this study, we implemented a new approach for the analysis of seismo‐volcanic data aimed at warning eruptions. We used advanced signal processing algorithms to analyze continuous seismic signals from a suite of well‐studied volcanoes (Mount St. Helens, Mt. Etna, Kilauea, Augustine, and Bezymianny) in order to create a new database of features found within the seismic signals. We found that pre‐eruptive variation in the Shannon entropy (a statistical parameter associated to the coherence of the seismic sources) of seismic signals offers a successfully feature for short‐term volcanic eruption alert. The relationship between pre‐eruptive seismic signals and Shannon entropy is based on changes in the probability distributions of the type of seismic waves, independent of the signal source. Key Points: While successful volcanic forecasting has been achieved, there is no generally valid model suitable for large range of eruptive scenariosWe used feature extraction to analyze seismic data from five well studied volcanoes to identify short‐term eruptive precursorsShannon entropy has a uniform temporal pattern of pre‐eruptive change and is a recurrent and transferable feature [ABSTRACT FROM AUTHOR]

Volcanic eruptions are amongst the most jeopardizing natural events due to their potential impacts on life, assets, and environment. In particular, atmospheric dispersal of volcanic tephra and aerosols during the explosive eruptions poses a serious threat to life and has significant consequences for infrastructures and global aviation safety. The volcanic island of Jan Mayen, located in the North Atlantic under trans-continental air traffic routes, is considered the northernmost active volcanic area in the world, with at least five eruptive periods recorded during the last 200 years. However, quantitative hazard assessments on the possible consequences for air traffic of a future ash-forming eruption are nonexistent. This study presents the first comprehensive long-term volcanic hazard assessment for Jan Mayen volcanic island in terms of ash dispersal and airborne tephra concentration at different flight levels. In order to delve in the characterization and modelling of that potential impact, a probabilistic approach based on merging a large number of numerical simulations is adopted, varying the volcano's Eruptive Source Parameters (ESPs) and meteorological scenario. Each ESP value is randomly sampled following a continuous Probability Density Function (PDF) defined from the Jan Mayen geological record. Over 20 years of climatic data are considered in order to explore the natural variability associated with meteorological conditions and used to run thousands of simulations of the ash dispersal model FALL3D on a 2 km-resolution grid. The simulated scenarios are combined to produce probability maps of airborne ash concentration, arrival time and persistence at different flight levels in the atmosphere. The resulting maps represent an aid to civil protection, decision makers and aviation stakeholders in assessing and preventing the potential impact from a future eruption at Jan Mayen. [ABSTRACT FROM AUTHOR]

Using RNN for automatic detection and classification of volcano seismic signals at Deception Island VolcanoManuel Titos (1), Angel Bueno (1), Luz García (1), Isaac Álvarez (1), Luciano Zuccarello (2), Jesus Ibanez (2), M. Carmen Benítez (1). (1) Departamento de Teoría de la Señal Telemática y Comunicaciones, Universidad de Granada, Spain(2) Departamento de Física Teórica y del Cosmos, Universidad de Granada, SpainSeismic monitoring of active volcanoes is a remote sensing technique used to determine seismic sources deep beneath volcanoes. As a result, the wide range of volcanic-seismic signals reflect the complex underlying physics of volcanic processes. Machine Learning and signal processing algorithms have emerged as a reliable approach to identify active volcanic sources. In this research, we propose a new framework for the detection and classification of volcano-seismic signals based on deep learning. Concretely, this work introduces recurrent neural networks (RNN), long short term memory (LSTM) and gated recurrent unit (GRU), to detect and classify continuous streams of volcanoseismic data. A representative dataset from Deception Island Volcano (Antarctica) containing volcano-tectonic earthquakes, long period events, volcanic tremors and hybrid events is used to train these models. Generalization capabilities are tested with data from the recent seismic survey performed at Deception Island Volcano in 2017 by the XXX Spanish Antarctic scientific expedition. Experimental results show that RNN, LSTM and GRU can exploit temporal and frequency information from continuous seismic data, attaining recognition accuracy above 90% of events correctly detected and classified. Further, despite the variations in geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, despite seismic sources changes over time.This work is supported by the following research projects: TEC2015-68752 (KNOWAVES, MINECO/FEDER); European Union's Horizon 2020 Research and Innovation Programme Under the Marie Sklodowska-Curie Grant Agreement no 798480 [ABSTRACT FROM AUTHOR]