Pattern recognition applied to seismic signals of Llaima volcano (Chile): An evaluation of station-dependent classifiers

Curilem, Millaray; Huenupán, Fernando; Beltrán, Daniel; Martín, César San; Fuentealba, Gustavo; Franco, Luis; Cardona, Carlos; Acuña, Gonzalo; Chacón, Máx; Khan, Muhammad Salman; Yoma, Néstor Becerra

doi:10.1016/j.jvolgeores.2016.02.006

Cited by 21 publications

(12 citation statements)

References 23 publications

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“…While previous studies have noted the presence of icequakes in the seismic record at the volcano (e.g. Curilem et al, 2014;Mora-Stock et al, 2014), they are apparently relatively rare compared to other ice-covered volcanoes (Métaxian et al, 2003;Jónsdóttir et al, 2009;Allstadt and Malone, 2014). Indeed, during our study period, OVDAS officially cataloged no icequakes as it is not within their mandate to do so (Fig.…”

Section: Discussionmentioning

confidence: 60%

“…Recent studies have attempted to construct automatic event classifiers for Llaima volcano using machine learning algorithms for pattern recognition with varying degrees of success (Curilem et al, 2014(Curilem et al, , 2018Soto et al, 2018). However, these studies either grouped the few identified cryogenic earthquakes with other earthquake types (Curilem et al, 2014), or excluded them from their training databases (Curilem et al, 2018;Soto et al, 2018). Therefore, the databases may have included a mixture of glacially-and magmatically-derived earthquakes that could have had an impact on their results.…”

Section: Llaima Volcanomentioning

confidence: 99%

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Investigating potential icequakes at Llaima volcano, Chile

Lamb¹,

Lees²,

Marı́n³

et al. 2019

Preprint

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Glacially- and magmatically-derived seismic events have been noted to heavily overlap in characteristics, thus there exists the potential for false-alarms or missed warnings at ice-covered volcanoes. Here we present the first study to specifically target icequakes at an ice-covered volcano in Southern Chile. Two months of broadband seismic data collected at Llaima volcano in 2015 were analyzed in order to quantify, characterize, and locate glacially-derived seismic events at one of the most active ice-covered volcanoes in the region. We find over 1,000 repeating seismic events across 11 families, the largest of which contains 397 events. Approximate locations and characteristics of the largest families lead us to conclude that these events were derived from persistent stick-slip motion along the ice-rock interface at the base of a glacier near the volcano summit. These results have implications for future seismic monitoring at Llaima volcano and other ice-covered active volcanoes in the region.

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Section: Discussionmentioning

confidence: 60%

Section: Llaima Volcanomentioning

confidence: 99%

Investigating potential icequakes at Llaima volcano, Chile

Lamb¹,

Lees²,

Marı́n³

et al. 2019

Preprint

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“…The number of neurons on the input layer is 2000 while in the hidden layer the number of neurons was varied from 50 to 200 in step of 50. The data used corresponds to 268 events, 200 were used for training and 76 for validation, which is considerably less amount of training data compared with the size of the training set used in previous papers [8,10,42]. Each event was coded into a binary representation, namely, the DNNs has 3 outputs, each one representing a different event (LP=[1 0 0], TR=[0 1 0] and VT=[0 0 1)].…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Firoozabadi

Seguel

Soto

et al. 2017

Journal of Electrical Engineering

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Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of recorded signals is a challenge for improving the study of the natural phenomena occurring inside these natural formations. The aim of this paper is the evaluation (denoising, localization and classification) and analysis of Llaima volcano activities, one of the most actives volcanoes in South America. Different already proposed methods, such as, Butterworth, Spectral Subtraction (SS) and Wiener Filter (WF) are compared to the proposed Modified Spectral Subtraction (MSS) and Modified Wiener Filter (MWF) to find the best method for denoising the volcano signals. Then, event localization based on received signals of volcano is performed. In this step, Time Delay Estimation (TDE)-based method is used on data acquired from 3 mechanical sensors located in the volcano area. The proposed method is used to estimate the area for event location. The proposed denoising methods make the starting point for the event more evident to increase the localization accuracy for events where the starting point is difficult to find. In the last step, a method based on the novel DNN technique is proposed to classify the three main events occurring in the Llaima volcano (TR (Tremor), LP (Long Period) and VT (Volcano Tectonic)).

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“…Supervised LR models have been applied in the estimation of landslide susceptibility [103] and to volcano seismic data to estimate the ending date of an eruption at Telica (Nicaragua) and Nevado del Ruiz (Colombia) [104]. SVM were applied many times to volcano seismology e.g., to classify volcanic signals recorded at Llaima, Chile [105] and Ubinas, Peru [106]. Multinomial Logistic Regression was used, together with other methods, to evaluate the feasibility of earthquake prediction using 30 years of historical data in Indonesia, also at volcanoes [107].…”

Section: Applications To Seismo-volcanic Datamentioning

confidence: 99%

Machine Learning in Volcanology: A Review

Carniel¹,

Guzmán²

2021

Updates in Volcanology - Transdisciplinary Nature of Volcano Science

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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.

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Pattern recognition applied to seismic signals of Llaima volcano (Chile): An evaluation of station-dependent classifiers

Cited by 21 publications

References 23 publications

Investigating potential icequakes at Llaima volcano, Chile

Investigating potential icequakes at Llaima volcano, Chile

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Machine Learning in Volcanology: A Review

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