Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano

Cárdenas-Peña, David; Orozco-Alzate, Mauricio; Castellanos-Domínguez, G.

doi:10.1016/j.cageo.2012.08.012

Cited by 23 publications

(6 citation statements)

References 14 publications

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“…A Bayesian classifier built on dissimilarity representations and k-nearest neighbor (k-NN) classifier were used, obtaining accuracy rates of 81% and 84%, respectively. Whereas in [26], the authors considered HB, LP, TRE, and VT events and worked on the stochastic variability of a wide set of time-variant features. With this approach, the classification rate improved from 78% to 88% when k-NN was used instead of HMM.…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Lara-Cueva

Benítez

Ruiz

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high accuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

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Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Lara-Cueva

Benítez

Ruiz

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…Each input waveform is 64 s long, and shorter waveforms are padded with 0 values to reach this length. Seismic data from between 2005-2006 at Nevado del Ruiz have previously been used to train and test an automatic classifier using Hidden Markov Models, which achieved a classification accuracy of approximately 88% (Cárdenas-Peña et al, 2013). This dataset was significantly smaller than the dataset used here and involved an extensive data pre-processing stage to extract frequency-based features and select the features which would provide the greatest information over time.…”

Section: Nevado Del Ruiz Datasetmentioning

confidence: 99%

“…Both models classify the majority of hybrid events as LP events (though hybrids represent a small proportion of the dataset), with the random classifier failing to identify any hybrid events. Previous automatic classifiers applied to Nevado del Ruiz data had a high misclassification rate of hybrid events, and it was suggested that discriminating between hybrid and LP events is particularly difficult from observations made at a single recording station (Cárdenas-Peña et al, 2013). Additionally, the a-priori classification of hybrid events may be subject to human analyst bias (Langer et al, 2006).…”

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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“…The selected features encapsulate the important aspects within data and synthesize them, providing an input for the unsupervised classifier [40]. Thus, the first step is to extract features from raw satellite data able to classify the most powerful explosive events.…”

Section: Feature Extractionmentioning

confidence: 99%

Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space

et al. 2021

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Stromboli volcano has a persistent activity that is almost exclusively explosive. Predominated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the explosive volcanic deposits emplaced during each explosion, and (iii) the height of the volcanic ash plume produced by the explosive events. We use optical satellite imagery to compute the land surface temperature (LST) and the ash plume height (PH). The magnitude of the explosive volcanic deposits (EVD) is estimated by using multi-temporal Synthetic Aperture Radar (SAR) intensity images. Once the input feature vectors were identified, we designed a k-means unsupervised classifier to group the explosive events at Stromboli volcano based on their similarities in two clusters: (1) paroxysms and (2) major explosions. The major explosions are identified by low/medium thermal content, i.e., LSTI around 1.4 °C, low plume height, i.e., PH around 420 m, and low production of explosive deposits, i.e., EVD around 2.5. The paroxysms are extreme events mainly characterized by medium/high thermal content, i.e., LSTI around 2.3 °C, medium/high plume height, i.e., PH around 3330 m, and high production of explosive deposits, i.e., EVD around 10.17. The centroids with coordinates (PH, EVD, LSTI) are: Cp (3330, 10.7, 2.3) for the paroxysms, and Cme (420, 2.5, 1.4) for the major explosions.

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Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano

Cited by 23 publications

References 14 publications

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

Automatic Recognition of Long Period Events From Volcano Tectonic Earthquakes at Cotopaxi Volcano

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

Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space

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