Evolution and dynamics of the open‑vent eruption at Arenal volcano (Costa Rica, 1968–2010): what we learned and perspectives

On 29 July 1968, there was a violent reactivation of Arenal volcano. The resulting westward-directed lateral blast eruption left two villages destroyed and 78 people dead. The activity continued as a long-lasting, open-vent eruption that evolved into seven recognisable phases refecting changes in magma supply, explosive activity and cone evolution, and ended in October 2010. Here, we review this activity, the geophysical approaches applied to understanding it and the open questions resulting from these insights. The eruptive dynamics were characterised by almost constant lava efusion, degassing, strombolian and vulcanian explosions and infrequent pyroclastic density currents. In this study, the total rock dense equivalent volume of lava and tephra erupted is calculated at 757±77 Mm3, while the volume of the lava fow feld is 527±58 Mm3. Typical seismic activity included harmonic and spasmodic tremors, long-period events and explosion signals with frequent audible “booms”. The decline of the eruptive activity started in 2000, with a decrease in the number and size of explosive events, a shift from long to short lava flows along with the collapse of lava fow fronts and the subsequent formation of downward-rolling lava block aprons, the frequent growth of dome-like structures on the summit and a gradual decrease in seismic energy. Multiple geological and geophysical studies during this 42-year-long period of open-vent activity at Arenal resulted in many advances in understanding the dynamics of andesitic blocky lava fows, the origin and diversity of pyroclastic density currents and seismic sources, as well as the role of site efects and rough topography in modifying the seismic wavefeld. The acoustic measurements presented here include two types of events: typical explosions and small pressure transients. Features of the latter type are not usually observed at volcanoes with intermediate to evolved magma composition. Explosions have diferent waveforms and larger gas volumes than pressure transients, both types being associated with active and passive degassing, respectively. This body of data, results and knowledge can inform on the type of activity, and associated geophysical signals, of open-vent systems that are active for decades.El 29 de julio de 1968 se produjo una violenta reactivación del volcán Arenal. La explosión lateral dirigida hacia el oeste dejó dos pueblos destruidos y 78 personas muertas. La actividad continuó como una erupción de larga duración a conducto abierto que evolucionó en siete fases reconocibles, las cuales reflejaron cambios en el suministro de magma, la actividad explosiva y la evolución del cono, y terminó en octubre de 2010. Aquí revisamos esta actividad, los enfoques geofísicos aplicados para entenderla, y las preguntas abiertas que resultan de este conocimiento. La dinámica eruptiva se caracterizó por una efusión de lava casi constante, desgasificación, explosiones estrombolianas y vulcanianas, e infrecuentes corrientes de densidad piroc- lástica. En este estudio, el volumen total de lava y tefra erupcionada en equivalente de roca densa se calcula en 757 ± 77 Mm3 , mientras que el volumen del campo de lavas es de 527 ± 58 Mm3 . La actividad sísmica típica incluía tremores armónicos y espasmódicos, eventos de largo periodo y señales de explosión con frecuentes bums audibles. El declive de la actividad eruptiva comenzó en el año 2000, con una disminución del número y el tamaño de los eventos explosivos, un cambio de coladas de lava largas a cortas, junto con el colapso de los frentes de colada de lava y la subsiguiente formación de abanicos de bloques de lava que se desplazaban ladera abajo, el crecimiento frecuente de estructuras tipo domo en la cima, y una disminución gradual de la energía sísmica. Los múltiples estudios geológicos y geofísicos realizados durante este período de 42 años de actividad a conducto abierto en el Arenal, dieron lugar a muchos avances en la comprensión de la dinámica de las coladas de lava blocosas andesíticas, el origen y la diversidad de las corrientes de densidad piroclástica y las fuentes sísmicas, así como el papel de los efectos de sitio sísmicos y la topografía accidentada en la modificación del campo de ondas sísmicas. Las mediciones acústicas presentadas aquí incluyen dos tipos de eventos: explosiones típicas y pequeños transitorios de presión. Las características de este último tipo no suelen observarse en volcanes con una composición de magma intermedia o evolucionada. Las explosiones tienen formas de onda diferentes y volúmenes de gas mayores que los transitorios de presión, y ambos tipos están asociados con la desgasificación activa y pasiva, respectivamente. Este conjunto de datos, resultados y conocimientos puede enseñarnos sobre el tipo de actividad y las señales geofísicas asociadas, de los sistemas a conducto abierto que permanecen activos durante décadas.Institut de Physique du Globe de ParisUniversidad de Costa Rica///Costa RicaUCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela Centroamericana de Geologí

Semantic segmentation of explosive volcanic plumes through deep learning

Tracking explosive volcanic phenomena can provide important information for hazard monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are visible-wavelength cameras, which are routinely deployed on volcanoes around the globe. Here, we present the development of deep learning models, based on convolutional neural networks (CNNs), to perform semantic segmentation of explosive volcanic plumes on visible imagery, therefore classifying each pixel of an image as either explosive plume or not explosive plume. We have developed 3 models, each with average validation accuracies of >97% under 10-fold cross-validation; although we do highlight that, due to the limited training and validation dataset, this value is likely an overestimate of real-world performance. We then present model deployment for automated retrieval of plume height, rise speed and propagation direction, all parameters which can have great utility particularly in ash dispersion modelling and associated aviation hazard identification. The 3 trained models are freely available for download at https://doi.org/10.15131/shef.data.17061509

Understanding eruption dynamics: insights from volcanic seismicity in Ecuador

Persistently active volcanoes in close proximity to society can pose a huge danger to infrastructure, lives and the economy. Careful monitoring of volcanic seismicity is integral to successful hazard assessment and risk management. Geophysical monitoring at active volcanoes can provide rich datasets to examine internal systems. Specifically, seismic monitoring offers the potential to develop real time analysis and forecasts. The generation of volcanic seismicity has been linked to processes such as magma ascent, degassing and rock fracturing. However, studies are often limited to individual volcanoes or specific episodes of unrest, and so it is difficult to compare interpretations. This aim of this thesis is twofold: to develop methodologies to better quantify and characterise volcanic seismicity, and to use these to provide new understanding of volcanic systems, the hazards they might pose and how we can better forecast and monitor unrest. First, I present an extensive literature review of our current understanding of volcanic seismicity. As there is no standardised procedure for the analysis of volcanic earthquakes, there are inconsistent uses of techniques and ambiguous terminology. Existing studies also tend to focus on a handful of well monitored volcanoes where dense arrays can be used to calculate source mechanisms and depths to interpret seismic swarms. In order to address this, I develop a thorough signal processing routine which generates a suite of metrics to characterise a single earthquake event. These metrics can be used across a sequence of earthquakes to track changes in the behaviour of seismicity, and distinguish different types of earthquakes. It is developed with poorly monitored volcanoes in mind, as metrics can be determined for signal from a single station, and even a single component instrument. I use parameters in the time domain including amplitude, duration and cross correlation, and compare three different approaches to calculate the quality (Q) factor, in the frequency domain. I then present two candidate volcanoes to apply the methodology and attempt to describe the internal processes at each. Tungurahua and Cayambe are two relatively understudied volcanoes and yet they are potentially the most dangerous natural hazards in Ecuador. Tungurahua’s most recent eruptive phase (1999-2016) was explosive and persistent. In contrast, Cayambe volcano has not erupted in over 200 years and yet has been seismically restless in recent years. This presents an opportunity to compare the seismicity associated with ongoing, and reawakening volcanic processes. In chapter 4, I characterise the seismicity atTungurahua between 2012 and the final explosions in 2016. Seismicity at Tungurahua was dominated by long-period (LP) earthquakes, particularly episodes of highly periodic, repeating LP seismicity, known as drumbeats. In this chapter, I show that persistent drumbeats occur in phase with cyclical Vulcanian eruptions. These events are attributed to the initial failure and subsequent resealing of an upper conduit plug. In each explosive episode, the signal metrics are able to distinguish a shift in the signal properties of drumbeat LPs. In chapter 5, I focus specifically on accelerating rates of drumbeat LPs, often considered precursors to eruptions. I use temporal statistics and a Markov chain Monte Carlo (MCMC) approach to model three episodes of drumbeats. In one significant episode, the last ever recorded drumbeats at Tungurahua, I show these events are precursors to a ‘failed’ attempt at an explosion. In chapter 6 I then compare these findings at Tungurahua, with the 2016 seismic crisis at Cayambe. Here I demonstrate the repeating LP seismicity is likely a result of shallow hydrothermal systems, rather than surficial ‘icequakes’ or magmatic ascent. However, swarms of volcano-tectonic events (VTs) in 2016, are likely attributed to stresses on regional faults and ascent of a new pulse of magma. Finally, I begin to explore the complex volcano-tectonic interactions at both Tungurahua and Cayambe. Where there are high rates of tectonic events globally, and high rates of eruptions, it is important to distinguish causality and coincidence. VT swarms at Cayambe occur two months after the Mw7.8 Pedernales earthquake, 200km west. Using models of static stress change I suggest the crust at Cayambe was subject to a dilational regime, prompting resumed activity in 2016. However, the Pedernales earthquake occurs just two months after the final eruption at Tungurahua and yet does not appear to promote or restrict further explosive activity. This thesis presents case studies of two active volcanoes that are subject to limited seismic monitoring. These methods are not computationally intensive and could be readily adopted into routine volcano monitoring, to further inform hazard assessment. Although Cayambe and Tungurahua are neighbouring volcanoes, comparable in their rheology, they are very different in their current dynamic state, and this is evident in the seismicity. An enhanced understanding of these systems should inform further assessment of seismicity at intermediate-composition, arc volcanoes

Caribbean plate and geodynamics

Two workshops were held at the LPI in January and February 1978 to discuss possible NASA research topics within the Caribbean area. The meetings explored these scientific topics, then focused on the few most interesting, namely current and past plate motions, earthquake prediction and volcanology.Minutes Compiled at LPI by Thomas R. McGetchin Assisted by Carolyn KohringSummary and Participant List--Letter to Dr. Edward A. Flinn with Recommended Program for NASA/OSTA Geodynamics Program--Agenda, Meeting #1--Agenda, Meeting #2--Summary of Meeting #1--Presentation and Enclosures/Meeting #1--Presentations and Enclosures/Meeting #

Volcanic Processes Monitoring and Hazard Assessment Using Integration of Remote Sensing and Ground-Based Techniques

The monitoring of active volcanoes is a complex task based on multidisciplinary and integrated analyses that use ground, drones and satellite monitoring devices. Over time, and with the development of new technologies and increasing frequency of acquisition, the use of remote sensing to accomplish this important task has grown enormously. This is especially so with the use of drones and satellites for classifying eruptive events and detecting the opening of new vents, the spreading of lava flows on the surface or ash plumes in the atmosphere, the fallout of tephra on the ground, the intrusion of new magma within the volcano edifice, and the deformation preceding impending eruptions, and many other factors. The main challenge in using remote sensing techniques is to develop automated and reliable systems that may assist the decision maker in volcano monitoring, hazard assessment and risk reduction. The integration with ground-based techniques represents a valuable additional aspect that makes the proposed methods more robust and reinforces the results obtained. This collection of papers is focused on several active volcanoes, such as Stromboli, Etna, and Volcano in Italy; the Long Valley caldera and Kilauea volcano in the USA; and Cotopaxi in Ecuador

Understanding magmatic processes and their timescales beneath the Tongariro Volcanic Centre through microanalytical investigations of the tephra record : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Sciences at Massey University, (Manawatū Campus), Palmerston North, New Zealand

Appendix A1 Published methodology was removed as it is ©Microscopy Society of America 2018. It may be accessed at https://doi.org/10.1017/S1431927618015428The Tongariro Volcanic Centre (TgVC) is a complex volcanic system located at the southern end of the Taupo Volcanic Zone in New Zealand, and has produced historical explosive eruptions of different eruptive styles. Its three ski fields and its iconic Tongariro Alpine Crossing attract more than 130,000 visitors annually. The last eruption occurred in 2012 on the northern flank of Tongariro, at the Te Maari vent. Due to the lack of precursory activity, this eruption could have turned into a tragedy if it had happened during day time. Previous studies have focused on the TgVC phenocrysts, which do not provide insights into shallow magmatic processes, essential to mitigate the resulting volcanic hazards. To understand magma ascent processes and their associated timescales, the textures and compositions of the micrometre-sized crystal cargo (i.e. microlites and micro-phenocrysts) carried during explosive eruptions are investigated, along with their conditions of crystallisation [i.e. P-T-X(H₂O)], which are constrained using hygrothermobarometry and MELTS modelling. Glass shards from five tephra formations spanning from c. 12 ka BP to 1996 AD, associated with explosive eruptions ranging from Strombolian to Plinian in style, are studied here. High resolution images and chemical maps of the tephras and the crystals are acquired using scanning electron microscopy (SEM) and secondary ion mass spectrometry. The variety of disequilibrium textures and compositions found in the micro-phenocrysts (< 100 μm) indicates multiple events of magma mixing, magma recharge, pressure fluctuations, and suggests an antecrystic origin. Crystal size distribution (CSD) of 60,000 microlites (< 30 μm) of plagioclase and pyroxene are generated from back-scattered-electron (BSE) images using a semi-automatic method developed here to undertake this study, employing the Weka Trainable Segmentation plugin to ImageJ. Combined with a well-constrained growth rate, crystallisation times are derived and indicate that microlites crystallised 2 to 4 days before the eruption, regardless of the eruption style. Microlite crystallisation occurred between mid-crustal depths and the surface (average of c. 4 km), at unusually high temperature for arc magmas of intermediate composition (average of 1076 °C), and at low water contents (average of 0.4 wt%). Considering the inferred depths and the crystallisation times of 2 to 4 days, ascent rates of only up to 9 cm s⁻¹ prior to shallow water exsolution are calculated. Vent exit velocities are not exceeding 27 m s⁻¹ after complete water exsolution, too slow to feed explosive eruptions characterised by supersonic exit velocities. This research proposes a new conceptual model for the magmatic plumbing system beneath TgVC, where the microlitic crystal cargos result from multiple intrusions of aphyric melts through dykes, which most of the time stall and evolve at depth as deep as the mid-crust. Eventually, a magma injection percolates through previous intrusions and entrains crystals of differing textures and histories. Dykes feeding volcanism funnel into a narrow cylinder towards the surface, allowing acceleration and triggering explosive eruptions. Therefore, the conduit geometry at TgVC is a key controlling factor on the explosivity, with narrower conduits resulting in more explosive eruptions, suggesting that volatile-poor magmas can still trigger explosive eruptions. This study supports that vertical foliation of the igneous upper crust is consistent with dyking and thus may be more common than typically acknowledged

Methodology for the recognition of non-stationary seismic-volcanic patterns using adaptive learning techniques

graficas, tablasEl monitoreo volcánico constituye una tarea imprescindible en el contexto de prevención y gestión del riesgo; en este sentido, los observatorios vulcanológicos y sismológicos cumplen una misión trascendental en la declaración de alertas tempranas de erupción volcánica. Y dentro de esta labor, la correcta clasificación de la sismicidad representa un insumo indispensable para la interpretación del fenómeno volcánico y la caracterización de dinámicas eruptivas; por tal motivo, es necesario que la clasificación se lleve a cabo de manera ágil y confiable. A través de la sismicidad correctamente etiquetada, los expertos analistas pueden caracterizar los procesos que estarían ocurriendo al interior de un volcán, e identificar precursores de una erupción. Sin embargo, la acertada discriminación de eventos sísmicos suele verse afectada por la migración de fuentes sísmicas, alteraciones en la dinámica de fluidos, cambios en los mecanismos de generación de grietas, entre otras situaciones, que pueden modificar la distribución de probabilidad de los registros sísmicos (cambios de concepto), y por tanto, incrementar la no estacionariedad de estas señales. Durante las últimas dos décadas, en las áreas de Aprendizaje Automático y Reconocimiento de Patrones se han desarrollado múltiples técnicas y herramientas aplicadas a enfoques de representación y clasificación de sismos volcánicos, entre las cuales destacan las redes neuronales, las máquinas de vectores de soporte, los modelos ocultos de Markov, entre otros, enmarcados (incluso) en contexto muy actuales como el Aprendizaje Profundo. En general, los estudios hallados al respecto en el estado del arte muestran resultados optimistas; sin embargo, se detalla que éstos son consecuencia de configuraciones experimentales restrictivas que disminuyen la complejidad del problema de clasificación planteado; una condición común es el uso de datos procedentes de periodos cortos de registro y poco representativos de la actividad volcánica. Esta limitación simula un entorno estacionario donde los modelos predictivos tradicionales funcionan eficazmente, pero que van en detrimento al actuar por un tiempo prolongado cuando los cambios de concepto se hacen evidentes. Siendo notable la necesidad de disponer de sistemas automáticos de clasificación que satisfagan las ``condiciones realistas'' del problema, como requerimiento esencial en la vigilancia volcánica, en esta tesis se propone el desarrollo de una metodología de reconocimiento de patrones sísmicos, a partir de registros de eventos volcánicos, que considere la adaptación de la clasificación a entornos y condiciones realistas y cambiantes. Para ello, se diseñó un modelo de clasificación centrado en el área del aprendizaje adaptativo y basado en aprendizaje incremental (aún no explorados en datos sísmicos), con el cual se trata el paradigma del cambio del concepto, de tal manera que algunas propiedades como la recurrencia continua de datos adquiridos, la naturaleza multiclase de los registros, los efectos geológicos y las restricciones de generalización en la clasificación, sean contempladas, aprovechadas y eventualmente contrarrestadas al momento de hacer la clasificación automática de los sismos (Texto tomado de la fuente)Volcanic monitoring is an essential task in the context of prevention and risk management; in this sense, the volcanological and seismological observatories fulfill a transcendental mission in the declaration of early warnings of volcanic eruptions. And within this labor, the correct classification of seismicity represents an indispensable supply for the interpretation of the volcanic phenomenon and the characterization of eruptive dynamics; for this reason, it is necessary to carry out the classification in an agile and reliable manner. Through correctly labeled seismicity, expert analysts may characterize the processes that would be taking place inside a volcano, and identify precursors of an eruption. However, the accurate discrimination of seismic events is usually affected by the migration of seismic sources, alterations in fluid dynamics, changes in crack generation mechanisms, among other situations. These conditions may modify the probability distribution of seismic records (concept drifts), and therefore, strengthen the non-stationarity of these signals. During the last two decades, multiple techniques and tools have been developed in Machine Learning and Pattern Recognition areas, and applied to representation and classification approaches of volcanic earthquakes. Neural networks, support vector machines, hidden Markov models are the most outstanding methods that have even been framed in very current contexts such as Deep Learning. In general, the studies found in this regard in the state of the art show optimistic results, however, they are the consequence of restrictive experimental configurations that decrease the complexity of the posed classification problem. A common condition is data usage from short periods of registration and unrepresentative of the volcanic activity. This limitation simulates a stationary environment where traditional predictive models work effectively, but their performance deteriorates when acting for a long time because concept changes become evident. The need to have automatic classification systems that satisfy the ``realistic conditions'' of the problem becomes evident, as an essential requirement in volcanic monitoring and eruption prediction. Therefore, this thesis proposes the development of a seismic pattern recognition methodology, based on records of volcanic events, which considers the adaptation of the classification to realistic and changing environments and conditions. For this, a classification model focused on the area of adaptive learning and based on incremental learning (not yet explored in seismic data) was designed, with which the concept drift paradigm is treated. This way, some properties such as the continuous arrival of acquired data, the multiclass nature of the records, the geological effects and the generalization restrictions in the classification are considered, exploited and eventually counteracted when automatically classifying the volcanic earthquakes.Este trabajo se ha llevado a cabo gracias al patrocinio económico del Programa Nacional de Formación de Investigadores, modalidad Doctorado Nacional, Convocatoria 617, de MINCIENCIAS (antes COLCIENCIAS).Declaración. Me permito afirmar que he realizado la presente tesis de manera autónoma y con la única ayuda de los medios permitidos y no diferentes a los mencionados en la propia tesis. Todos los pasajes que se han tomado de manera textual o figurativa de textos publicados y no publicados, los he reconocido en el presente trabajo. Ninguna parte del presente trabajo se ha empleado en ningún otro tipo de tesis.DoctoradoDoctor en IngenieríaReconocimiento de patronesEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale