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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.

The time scale of ground displacement at volcanoes varies between short, sub second seismic events, to days, months or even years. This study is focused on data from seismic and GNSS stations located around Mount Etna. The GNSS and seismic stations operate at different time scales. Data from these different time scales is extracted and combined in order to better understand the subsurface dynamics. The overall aim of this research is to improve volcanic forecasting and monitoring. It does this in a novel way by applying signal processing and machine learning techniques to the rich dataset. Mount Etna offers an interesting case study as it is a widely monitored volcano with a variety of sensors and with a rich pool of data to analyse. Additionally the volcanic dynamics at Mount Etna are complex. This is a volcano where there is a variety of different sub-surface dynamics due to the movement of both deep and shallow magma. This allows for rich insights to be drawn through the combination of different signal types. This study looks at combining the information obtained from the seismic array at Mount Etna, with the information obtained from various GNSS stations on the volcano. The seismic array has been able to capture ground velocity data in the frequency range 0.025 Hz to 50 Hz from a range of stations at different locations across the volcano. The GNSS stations measure ground displacement with a sampling frequency of 1 Hz, and they allow for longer term ground dynamic analysis. We analyse different seismic events, and relate the type and number of the seismic events to the long term ground deformation that we see in the recorded GNSS data. Where links between the two signal types have been identified, research is ongoing to establish a direct connection with known volcanic activity on Mount Etna. This will help establish what the relationship that we are seeing signifies. This integration of data from different types of sensors is a significant step into bridging the gap between seismic and quasi-static ground displacement at active volcanoes and should open the path toward more in depth volcanic monitoring and forecasting.

The overwhelming success of data-driven models to solve complex predictive real-world problems has made them an effective alternative to the simulation-driven models. In addition to their computational cost, simulation-driven approaches need to be calibrated by actual data that links both to the physical theory and thereby improves both our knowledge and existing models. Similarly, data-driven methods allow deepening the knowledge by analyzing existing data. Therefore, to develop improved predictive models, one needs to pursue a balance between both data-driven and simulation-driven approaches, keeping data as a common pivot. This can be done by using Machine Learning (ML) which is a powerful tool to extract knowledge directly from the data and provide complementary information to the previously developed physics-based models. The main advantage of ML methods is their ability to process massive and complex structure data sets that are difficult to be processed by traditional data-processing methods. Therein lies also the main disadvantage of ML methods i.e., they need massive amounts of data that often are not available. In this study however we take advantage of a new physics-based model of the earthquake fault system of the Southwest Iceland transform zone and generate synthetic, but physically realistic, finite-fault earthquake catalogues. For each earthquake in the catalogues we simulate seismicground motion parameters at a large hypothetical station network thus generating a massive parametric dataset of synthetic seismic data from earthquakes of magnitude 5 to 7. We will apply multiple types of new generation machine learning techniques such as deep neural network (DNN), deep Bayesian neural networks (DBNN) and deep Gaussian processes (DGP) to investigate the ability and efficiency of the methods in capturing the characteristics of the synthetic dataset in terms of key parameters e.g., ground motion amplitudes, ground motion attenuation versus source-to-site distance and site effects and independent parameters such as earthquake magnitude, fault extend and depth, etc. The ML methods will be trained using a procedure known as greedy layer-wise pretraining where each layer is initialized via the unsupervised pretraining, and the output of the previous layer can be used as the input for the next one. A typical advantage of these pre-trained networks compared to the other deep learning models is that the weights initialization renders the optimization process more effective, providing faster convergence by initializing the network parameters near a convergence region. This can help to avoid underfitting/overfitting problems when the training samples are highly correlated. The results will provide a new insight into the efficiency and usefulness of ML methods on synthetic seismic datasets with implications for their use on actual and more sparser datasets.

The creation of hazard maps relative to volcanic phenomena requires taking into account the intrinsic complexity and variability of eruptions. Here we present an example of how HPC can allow producing a high resolution multi-source probabilistic hazard assessment due to tephra fallout over a domain covering Southern Italy. The three active volcanoes in the Neapolitan area, Somma-Vesuvius, Campi Flegrei and Ischia, were considered as volcanic sources. For each one, we explored three explosive size classes (Small, Medium and Large) for Somma Vesuvius and Campi Flegrei, and one explosive size class (Large) for Ischia. For each size class, we performed 1500 numerical simulations of ash dispersion (total of 10500) using the Fall3D (V8.0) model over a computational domain covering Southern Italy with a 0.03° ⨉ 0.03° (~3 km ⨉ 3 km) resolution. Within each size class, the eruptive parameters have been randomly sampled from well-suited probability distributions and with different meteorological conditions, obtained by randomly sampling a day between 1990 and 2020 and retrieving the relative data from the ECMWF ERA5 database. This allowed exploring the intra-class variability and to quantify aleatoric uncertainty. The results of these simulations have been post-processed with a statistical approach by assigning a weight to each eruption (based on its eruption magnitude) and the annual eruption rate of each size class. For the case of Campi Flegrei, the variability in the eruptive vent position has also been explored by constructing a grid of possible vent locations with different spatial probability. By merging the results obtained for each source and size class we produced a portfolio of hazard maps showing the expected mean annual frequency of overcoming selected thresholds in ground tephra load. A disaggregation analysis has also been performed in order to understand which particular source and/or size class had the greater impact on a particular area. The completion of this work, considering both numerical simulations and the statistical elaboration of the results has required a total of more than 5000 core hours and the processing of more than 2TB of data, an effort that wouldn’t have been possible without the access to high level HPC resources.

The South Iceland Seismic Zone (SISZ) and Reykjanes Peninsula Oblique Rift (RPOR) in Southwest Iceland together form one of the two major transform zones in the country that have the greatest capacity for the occurrence of destructive earthquakes. Therefore, in these regions, the seismic hazard is highest and performing a probabilistic seismic hazard assessment (PSHA) is vital as the foundation of earthquake resistant building design and seismic risk mitigation. It is well known both from observations as well as physics-based (PB) modeling of earthquake rupture and near-fault ground motion simulations, that the most damaging part of near-fault seismic motion is the velocity pulse, the large-amplitude and long-period pulse-like ground motions found along the fault and away from the ends of strike-slip faults. Such motions cause intense earthquake action primarily on large buildings, such as hydroelectric power plants, dams, powerlines, bridges and pipelines. However, the data is still too limited to enable the reliable calibration of a physically realistic, yet parsimonious, near-fault model that incorporate such effects into empirical ground motions models (GMMs), thereby allowing their incorporation into a formal PSHA. However, in the recent European H2020 ChEESE project, we established a new 3D finite-fault system model for the SISZ-RPOR system that now has facilitated the simulation of finite-fault earthquake catalogues. Moreover, the catalogues have been implemented into the CyberShake platform, the PB earthquake simulator that was adapted to the characteristics of the SISZ-RPOR earthquakes in the ChEESE project. The seismic ground motions of each earthquake in the catalogue have thus been simulated on a dense grid of 594 near-fault stations in Southwest Iceland. The simulation has been carried out on high-performance computing systems of the Barcelona Supercomputing Centre in Spain. Moreover, the hypocentral locations and slip distributions on each synthetic fault have been varied, resulting in approximately 1 million earthquake-station-specific pairs of synthetic low-frequency and high-amplitude near-fault ground motion time histories. In this study, we analyse this dataset using an artificial neural network to reveal its characteristics in terms of amplitudes and the characteristics of near-fault velocity pulses, capturing all key features of such effects. The results will facilitate the incorporation of the near-fault effects into new near-fault and far-field GMMs, that are a key element of conventional PSHA. This will both enable the near-fault PB-PSHA along with the comparison of PSHA from the synthetic dataset vs. the GMMs. This will usher in a new era of PB-PSHA in Iceland.

The search for pre-eruptive observables that can be used for short-term volcanic early warning remains a scientific challenge. Pre-eruptive patterns in seismic data are usually identified by analyzing seismic catalogues (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 successful 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 used as short-term eruptive precursors. We quantified how the Shannon entropy drops several hours before the eruptions analyzed, between 4 days and 12 h before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index) and complementary information on types of seismic sources, the meaning of physical changes in the volcanic system could be obtained. Our results show that pre-eruptive variation in Shannon entropy offers is a confident short-term volcanic eruption forecasting tool.

Nowadays, tephra fallout hazard is based on coupling the physical modeling of the tephra dispersion processes with a probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position and meteorological conditions. In this framework, we present a prototypal methodology to carry out a multi-volcano long-term tephra fallout hazard assessment in Southern Italy from the active Neapolitan volcanoes: Somma-Vesuvius, Campi Flegrei, and Ischia. FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1,500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high-resolution volcanic hazard assessment, in order to quantify the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-years exposure time, and the relative epistemic uncertainty. This work provides, for the first time, a multi-volcano probabilistic hazard analysis for tephra fallout, fully comparable with those used for seismic phenomena and other natural disasters in which multiple sources are integrated together, and it accounts for potential changes in regimes of each single considered volcano. This allows us to discuss also how the full information can be traced back to provide specific information about the prevalence of different volcanoes and eruptive style in the different target areas, based on hazard disaggregation. The methodology is applicable to any other volcanic areas or over different exposure times.

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026)., Future Generation Computer Systems, 146, ISSN:0167-739X, ISSN:1872-7115

The 2021 Fagradalsfjall basaltic fissural eruption in Iceland was closely studied due to its proximity to Reykjavík, which allowed easy installation and maintenance of monitoring equipment. Here we present the results from a network of calibrated webcameras maintained by the Icelandic Meteorological Office and Department of Civil Protection and Emergency Management which were used to monitor volcanic plume height and fire fountain height. A number of different camera designs optimised for different power and communications constraints were used, some built in house at IMO, and they will be presented here. To make a 3D height measurement from a 2D web camera image requires extra geometric constraints, which are provided by assuming the vent location and wind direction, in a similar manner to the method applied at Etna. We have implemented this technique as a react.js single page app, which is kept updated by a messaging queue system which pushes new images through the servers at IMO. Additionally, the webcameras have to be calibrated, in that the geometry of the camera and lens distortion parameters have to be known - this is either perfomed in the laboratory, or where the cameras were not available before installation, using one of a number of vicarious calibration techniques developed for this purpose. The resulting plume heights were used to constrain SO2 dispersion models that were the basis for air quality forecasts during the eruption.

Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at “Volcan de Fuego” , Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.

We present an S-phase picking algorithm for volcano-tectonic earthquakes (VTs), based on the changes of frequency and amplitude expected in the plane transverse to the ray direction at S-phase arrival. A measure of these changes, called spectral dissimilarity , is proposed. Picking is performed in a particular waveform transformation that underlines such variations foreseen: horizontal instant power. Then, the algorithm provides a measure of its reliability, grounded on the low or high fluctuations of the picking instant obtained when applied to other horizontal components of the seismogram. Experiments are performed to test the algorithm with a challenging database of volcano seismic earthquakes from Mt. Etna, carefully picked and labeled by a human expert. The technique is compared to two well-known S-phase pickers: one based on the damped predominant period analysis and the other based on polarization and kurtosis rate analysis. The algorithm improves these techniques for the particular scenario of VTs, providing interesting results and possibilities of the application.

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.

En 1847 los alsacianos Daniel Dollfus-Ausset y Guillaume-Philippe Schimper realizaron una expedición a Sierra Nevada, en el sur de España. Su objetivo era la búsqueda de una nueva especie de cabra montes, pero su interés se extendía a la geografía, el paisaje, el glaciarismo y la botánica. En aquella expedición se realizó la primera fotografía al daguerrotipo que se conoce de Sierra Nevada, y se encontraron restos de antiguos glaciares cuaternarios sobre los que elaboraron una teoría mantenida como válida durante bastante tiempo. En este artículo se describe la personalidad de los viajeros, las circunstancias de su viaje y lo que representa el mismo en la historia científica de Sierra Nevada.En 1847, les Alsaciens Daniel Dollfus-Ausset et Guillaume-Philippe Schimper firent une expédition en Sierra Nevada,dans le sud de l’Espagne. Son but était de rechercher une nouvelle espèce de chèvre de montagne, mais son intérêt s’est étendu à la géographie, au paysage, au glacierisme et à la botanique. Au cours de cette expédition, une photographie au daguerréotype de Sierra Nevada a été prise pour la première fois. De même, des restes d’anciens glaciers du Quaternaireont été identifiés, et sur ceux-ci, ils ont développé une théorie maintenue comme valable pendant un certain temps. Cet article décrit la personnalité des voyageurs, les circonstances de leur voyage et ce que cette expérience représente dans l’histoire scientifique de la Sierra Nevada.In 1847 the Alsatians Daniel Dollfus-Ausset and Guillaume-Philippe Schimper made an expedition to Sierra Nevada, insouthern Spain. His goal was to search for a new species of mountain goat, but his interest extended to geography, landscape, glacierism and botany. In that expedition, the first daguerreotype photograph of Sierra Nevada was taken, and remains of ancient Quaternary glaciers were identified (on which they developed a theory maintained as valid for quite some time). This article explains the personality of the travelers,the circumstances of their trip and what it represents in the scientific history of Sierra Nevada.

This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995–2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016–2017 by the Spanish Antarctic scientific campaign. Despite the variations in the 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, even if seismic sources change over time.

Campi Flegrei is an active volcano located in one of the most densely inhabited areas in Europe and under high-traffic air routes. There, the Vesuvius Observatory’s surveillance system, which continuously monitors volcanic seismicity, soil deformations and gas emissions, highlights some variations in the state of the volcanic activity. It is well known that fragmented magma injected into the atmosphere during an explosive volcanic eruption poses a threat to human lives and air-traffic. For this reason, powerful tools and computational resources to generate extensive and high-resolution hazard maps taking into account a wide spectrum of events, including those of low probability but high impact, are important to provide decision makers with quality information to develop short- and long- term emergency plans. To this end, in the framework of the Center of Excellence for Exascale in Solid Earth (ChEESE), we show the potential of HPC in Probabilistic Volcanic Hazard Assessment. On the one hand, using the ChEESE's flagship Fall3D numerical code and taking advance of the PRACE-awarded resources at CEA/TGCC-HPC facility in France, we perform thousands of simulations of tephra deposition and airborne ash concentration at different flight levels exploring the natural variability and uncertainty on the eruptive conditions on a 3D-grid covering a 2 km-resolution 2000 km x 2000 km computational domain. On the other hand, we create short- and long-term workflows, by updating current Bayesian-Event-Tree-Analysis-based prototype tools, to make them capable of analyze the large amount of information generated by the Fall3D simulations that finally gives rise to the hazard maps for Campi Flegrei.

Jan Mayen Island (Norway), located in the North Atlantic, is considered the world’s northernmost active subaerial volcano, with at least five eruptive periods recorded during the last 200 years. Explosive activity of the volcano may seriously affects the nearby important air traffic routes. However, no quantitative studies on the possible impact of a new explosive volcanic eruption on the air traffic have been conducted. In this work, we statistically characterise the spatial and temporal distribution of airborne volcanic ash cloud and its persistence at different flight levels. Since current operational forecast products do not always meet the requirements of the aviation sector and related stakeholders (using coarse time and space scales, with outputs on a 40 km horizontal resolution grid and 6 hour time averages), and they neglect epistemic/aleatory uncertainties in quantitative forecasts on real time, we propose hourly high resolution hazard maps over a 3D-grid covering a 2 km-resolution spatial domain 2000 km x 2000 km wide. We present the use of high-performance computing (HPC) to overcome the computational limitations associated with unbiased long-term probabilistic volcanic hazard assessment (PVHA) .Considering a continuum of possible combinations of Eruptive Source Parameters (ESP) to assess and quantify the uncertainty, and the natural variability associated with wind fields over 20 years of data, from 1999 to 2019, we run thousands of analytical solutions (numerical simulations) using the most recent version of the FALL3D model. As a result, the first comprehensive long-term PVHA for Jan Mayen volcanic island is presented.

Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based on fully connected DNNs. Two DNN architectures with different weights scheme initialization are studied: stacked denoising autoencoders and deep belief networks. Using a combined feature vector of linear prediction coefficients and statistical properties, we evaluate classification performance on seven different classes of isolated seismic events. These proposed architectures are compared to multilayer perceptron, support vector machine, and random forest. Experimental results show that DNNs can efficiently capture complex relationships of volcano-seismic data and achieve better classification performance with faster convergence when compared to classical models.

This work presents a novel approach to automatic detection of long period events (LP) in continuous seismic records. Without any supervised learning, the proposal is based on a simple processing to search for the LP characteristic shape, duration, and band of activity. Continuous raw signals from the seismometer are first filtered into three frequency bands separating lower, central, and upper frequency components. These new signals are then processed in parallel to extract subband envelopes and create a characteristic function that enhances LP features. Experiments to test the proposal are presented using: 1) 2 h of continuous recordings of the Volcano of Deception Island, Antarctica, containing LP events artificially contaminated with seismic background noise to create low signal-to-noise ratio scenarios and 2) a set of earthquake-like computer generated signals, randomly produced and inserted in the continuous records to recreate a testing environment as challenging as possible. A receiver operating curve analysis of the results compared to those of a classical short/long time average approach, provides positive conclusions on the performance of the technique presented.

Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic monitoring deals with such a large amount of data that it would become an overwhelming job for an operator to do manually. Therefore the use of automatic detection and classification techniques based on the Machine Learning approach are suitable in meeting such a challenge. The aim of this work is to test the capability of the Deep Neural Network (DNN) by using different event parametrization as a confident classifier tool that could permit a reliable seismic catalog to be built in a new and un-analyzed volcanic scenario. We tested different configurations in order to build an approach that was as simple as possible to use this classifier with a limited number of events. In this regard, the feature space was explored in order to select the most significant parameters of the seismic signals. The data used for this analysis corresponds to the Planchon Peteroa Volcanic Complex (PPVC) located in the Transitional Southern Volcanic Zone (TSVZ) between Chile and Argentina, South America. The most significant result of this work was not only that it provided an analysis in terms of performance of this algorithm, especially when the training, validation and test dataset is reliable although definitely reduced, but it also gave an insight of into how an optimal event parametrization can significantly improve the automatic detection and classification of seismo-volcanic events.

The accurate estimation of the arrival time of seismic waves or picking is a problem of major interest in seismic research given its relevance in many seismological applications, such as earthquake source location and active seismic tomography. In the last decades, several automatic picking methods have been proposed with the ultimate goal of implementing picking algorithms whose results are comparable to those obtained by manual picking. In order to facilitate the use of these automated methods in the analysis of seismic traces, this paper presents a new free, open source, software graphical tool, named APASVO, which allows picking tasks in an easy and user-friendly way. The tool also provides event detection functionality, where a relatively imprecise estimation of the onset time is sufficient. The application implements the STA-LTA detection algorithm and the AMPA picking algorithm. An autoregressive AIC-based picking method can also be applied. Besides, this graphical tool is complemented with two additional command line tools, an event picking tool and a synthetic earthquake generator. APASVO is a multiplatform tool that works on Windows, Linux and OS X. The application can process data in a large variety of file formats. It is implemented in Python and relies on well-known scientific computing packages such as ObsPy, NumPy, SciPy and Matplotlib.

Whilst recent advances in the field of artificial neural networks could be applied to monitor volcanoes, its direct application remains a challenge given the complex geodynamics involved and the size of available datasets. However, Bayesian Neural Networks (BNNs) are probabilistic models that could classify and provide uncertainty estimation for transient seismic sources, even under data scarcity conditions. This research focuses on practical applications of BNNs to classify volcano-seismic signals using two variational learning approaches: Bayes by back-prop and Monte-Carlo dropout. We evaluate classification performance on seven classes of isolated events registered at “Volcan de Fuego”, Colima. Experimental results show an overall improvement for Monte-Carlo dropout approximation when compared to Bayes by backprop. Being at the intersection of Bayesian learning and geophysics, we demonstrate that BNNs provide uncertainty estimations when internal volcano-seismic sources change, which undoubtedly helps to enhance current early warning systems at volcanic observatories.

This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers originated during the TOMO-ETNA experiment. Air-gun shots produced by the vessel “Sarmiento de Gamboa” and contemporary passive seismicity occurring in the island are recorded by a dense network of stations deployed for the experiment. In such scenario, automatic processing is needed given: (i) the enormous amount of data, (ii) the low Signal-to-Noise ratio of many of the available registers and, (iii) the accuracy needed for the velocity tomography resulting from the experiment. A preliminary processing is performed with the records obtained from all stations. Raw data formats from the different types of stations are unified, eliminating defective records and reducing noise through filtering in the band of interest for the phase picking. The Advanced Multiband Picking Algorithm (AMPA) is then used to process the big database obtained and determine the travel times of the seismic phases. The approach of AMPA, based on frequency multiband denoising and enhancement of expected arrivals through optimum detectors, is detailed together with its calibration and quality assessment procedure. Examples of its usage for active and passive seismic events are presented.