Understanding the timing of eruption end using a machine learning approach to classification of seismic time series

Manley, Grace F.; Pyle, David M.; Mather, Tamsin A.; Rodgers, Mel; Clifton, David A.; Stokell, Benjamin G.; Thompson, Glenn; Londoño, John Makario; Roman, Diana C.

doi:10.1016/j.jvolgeores.2020.106917

Cited by 13 publications

(13 citation statements)

References 60 publications

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“…Data are rarely perfectly IID in practice; however, we hypothesize that this approximation holds to a first-order and observe whether this hypothesis is supported by the resulting models distinguishing between previously unseen eruptive and non-eruptive data. This assumption was shown to be acceptable to a first order in previous work involving these classification methods (Manley et al, 2020) and we therefore retain this assumption for the seismic features. In this paper, we also assume that the individual gas and geodetic changes will be approximately IID for similar reasons to the seismic data: the generative process which produces measured geodetic and gas emission rates is assumed to not have memory of values on previous days.…”

Section: Assumptionsmentioning

confidence: 95%

“…Ren et al (2020) applied both supervised and unsupervised classification to seismic data at Piton de la Fournaise, La Réunion and identified consistent tremor frequencies between eruptive episodes. Manley et al (2020) identified transitions based on supervised classification of data from Telica and Nevado del Ruiz volcanoes, and showed that machine learning classification of features derived from seismic data alone held the potential to identify the end of eruptive sequences. Additionally, the timing of transition between eruptive and non-eruptive seismic activity at the end of eruption was found to be 2-4 months after the last visual activity observed at the volcano.…”

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confidence: 99%

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Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

et al. 2021

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Understanding the timing of critical changes in volcanic systems, such as the beginning and end of eruptive behavior, is a key goal of volcanic monitoring. Traditional approaches to forecasting these changes have used models motivated by the underlying physics of eruption onset, which assume that geophysical precursors will consistently display similar patterns prior to transition in volcanic state. We present a machine learning classification approach for detecting significant changes in patterns of volcanic activity, potentially signaling transitions during the onset or end of volcanic activity, which does not require a model of the physical processes underlying critical changes. We apply novelty detection, where models are trained only on data prior to eruption, to the precursory unrest at Augustine Volcano, Alaska in 2005. This approach looks promising for geophysically monitored volcanic systems which have been in repose for some time, as no eruptive data is required for model training. We compare novelty detection results with multi‐class classification, where models are trained on examples of both non‐eruptive and eruptive data. We contextualize the results of these classification models using constraints from petrological, satellite and visual observations from the 2006 eruption of Augustine Volcano. The transition from non‐eruptive to eruptive behavior we identify in mid‐November 2005 is in agreement with previous estimates of the initiation of dike intrusion prior to the 2006 eruption. We find that models which include multiple types of data (seismic, deformation, and gas emissions) can better distinguish between non‐eruptive and eruptive data than models formulated on single data types.

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Section: Assumptionsmentioning

confidence: 95%

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confidence: 99%

Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

et al. 2021

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“…This study used the full spectrogram as input vector and using ~7% of analyst-labelled events for training achieved a classification accuracy of approximately 77%. Hand-crafted features derived from this catalogue of detected events have previously been used to classify eruptive and non-eruptive activity during the 2012 eruption of Nevado del Ruiz (Manley et al, 2020).…”

Section: Nevado Del Ruiz Datasetmentioning

confidence: 99%

A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

et al. 2022

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Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.

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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].…”

Section: Applications To Seismo-volcanic Datamentioning

confidence: 99%

“…However, a RF trained with 2009-2011 data did not perform well on data recorded in 2014-2015, demonstrating how difficult it is to generalize models even at the same volcano [108]. RF, together with other methods, was recently used on volcano seismic data with the specific purpose to determine when an eruption has ended [104], a problem which is far from being trivial. RF was also used to derive ensemble mean decision tree predictions of sudden steam-driven eruptions at Whakaari (New Zealand) [109].…”

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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Understanding the timing of eruption end using a machine learning approach to classification of seismic time series

Cited by 13 publications

References 60 publications

Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

Machine Learning in Volcanology: A Review

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