Chapter Machine Learning in Volcanology: A Review

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches

COMPREHENSIVE ANALYSIS OF SEISMIC SIGNALS FROM PACAYA VOLCANO USING DEEP LEARNING EVENT DETECTION

Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, including permanent and temporary arrays from 2015 to 2022. Initially over 12,000 independent events were detected although most were unlocatable. A predetermined 1D velocity model calculated by Lanza & Waite (2018) was initially used to locate the earthquakes. This velocity model was updated using VELEST and the locations were calculated using new 1D P-wave and S-wave velocity models. This resulted in 512 events after a quality control filtering process. These events ranged in depths from -2.5 km (summit of Pacaya) to 0 km (sea level) all located directly beneath the vent. The detection process took about 2-3 hours per 15 days on each 3-component broadband seismometer. The method shows promise in providing an efficient and effective method to pick volcano tectonic seismic events, and it did well identifying the emergent arrivals in these datasets; however, it has shortcomings in detecting some low-frequency event types. This could be addressed through additional training of the algorithm. The very low speeds in our new P-wave and S-wave velocity models highlight the poor consolidation of the young MacKenney cone. Further study is encouraged to better understand the accuracy and type of earthquakes picked, especially the increased level of activity during or leading up to an eruption at Pacaya volcano

Machine Learning in Volcanology: A Review

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches

Combining filter based feature selection methods and gaussian mixture model for real-time seismic event classification at Cotopaxi volcan

This paper proposes an exhaustive evaluation of five different filter-based feature selection methods in combination with a Gaussian Mixture Model classifier for almost real time classification of Long-Period and Volcano-Tectonic seismic events recorded at Cotopaxi volcano in Ecuador between 2009 and 2010...Este artículo propone una evaluación exhaustiva de cinco diferentes métodos de selección de características basados en filtrado, en combinación con el Modelo Mixto Gaussiano como clasificador para la categorización prácticamente en tiempo real de eventos sísmicos Largo-Período y Vulcano-Tectónico grabados en el volcán Cotopaxi en Ecuador entre 2009 y 2010..

Deep-learning for volcanic seismic events classification

In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 models reached the worst results due to the overfitting, and this happened due to the small number of samples per class employed to train these complex models...En este trabajo, proponemos un nuevo método para clasificar entre spectrograms Long-Period y Volcano-Tectonic utilizando seis diferentes arquitecturas de conocimiento profundo. El método desarrollado utiliza tres redes neuronales convolucionales llamadas: DCNN1, DCNN2 y DCNN3. De igual manera tres redes neuronales convolucionales son combinadas con redes neuronales recurrentes llamadas: DCNN-RNN1, DCNN-RNN2, y DCNN-RNN3 para maximizar el valor del area bajo la curva (ROCAUC) en un datases de espectrogramas de eventos sísmicos volcánicos. Los modelos DCNN-RNN1, DCNN-RNN2, y DCNN-RNN3 alcanzaron los desempeños más bajos debido a que presentaron overfitting, y esto puede ser a causa de la pequeña cantidad de muestras por clase utilizadas para entrenar estos modelos ta complejos..

Deep-Learning for Volcanic Seismic Events Classification

In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images...En este trabajo, proponemos un nuevo método para clasificar entre spectrograms Long-Period y Volcano-Tectonic utilizando seis diferentes arquitecturas de conocimiento profundo. El método desarrollado utiliza tres redes neuronales convolucionales llamadas: DCNN1, DCNN2 y DCNN3. De igual manera tres redes neuronales convolucionales son combinadas con redes neuronales recurrentes llamadas: DCNN-RNN1, DCNN-RNN2, y DCNN-RNN3 para maximizar el valor del area bajo la curva (ROCAUC) en un datases de espectrogramas de eventos sísmicos volcánicos..

Volcanic Seismic Event Classification basedon CNN Architectures

This paper explores the use of convolutional neural network architectures in the context of volcanic seismic event classification through the use of gray-level spectrogram images of longperiod and volcano-tectonic seismic events. We combined the architectures with a set of hyperparameter configurations that produced 720 classification models, which were able to learn the morphological pattern described by the gray-level spectrogram images of seismic events...Este artículo explora el uso de arquitecturas de redes neuronales convolucionales en el contexto de clasificación de eventos sísmicos volcánicos mediante el uso de imágenes de espectrogramas en escala de grises de eventos sísmicos de período largo y volcano-tectónicos. Combinamos las arquitecturas con un conjunto de configuraciones de hiperparámetros que produjeron 720 modelos de clasificación, los cuales fueron capaces de aprender los patrones morfológicos descritos por las imágenes de espectrogramas en escala de grises..

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

Pattern of cryospheric seismic events observed at Ekström ice shelf, Antarctica

Mobility of glaciers such as rapid retreat or disintegration of large ice volumes produces a large variety of different seismic signals. Thus, evaluating cryospheric seismic events (e.g. changes of their occurrence in space and time)allows to monitor glacier dynamics. We analyze a one year data span recorded at the Neumayer seismic network in Antarctica. Events are automatically recognized using hidden Markov models. In this study we focused on a specifc event type occurring close to the grounding line of the Ekström ice shelf. Observed waveform characteristics are consistent with an initial fracturing followed by the resonance of a water filled cavity resulting in a so-called hybrid event. The number of events detected strongly correlates with dominant tide periods. We assume the cracking to be driven by existing glacier stresses through bending. Voids are then filled by sea water, exciting the observed resonance. In agreement with this model, events occur almost exclusively during rising tides where cavities are opened at the bottom of the glacier, i.e. at the sea/ice interface