Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano

Venegas, Pablo; Pérez, Noel; Benítez, Diego S.; Lara-Cueva, Román; Ruiz, Mario

doi:10.1109/jstars.2019.2916045

Cited by 22 publications

(7 citation statements)

References 33 publications

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“…Progressive damage of the rocky slope is a precursor of slope instability, characterised by seismic events triggered by a rapid release of energy. Therefore, continuous seismic monitoring through an array of surface or near surface sensors, together with seismic analysis, comprising detection, classification, and localisation, has been gaining traction recently, whether events are induced by volcanic activity [ 2 ], landslides [ 3 ] or mining [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…The main challenges in classifying seismic signals are: (1) lack of open access annotated datasets [ 11 ]; (2) imbalanced catalog of labeled events, caused by the sparsity of events of interest [ 11 ]; (3) high similarities between unknown natural and anthropogenic “interfering” signals and events of interest in time and/or frequency domain [ 12 ]. Feature engineering is a key step towards efficient signal classification as a large set of features with redundant information could easily increase the processing time and cause classifier overfitting, multicollinearity, and suboptimal feature ranking at the selection stage [ 2 ]. Feature construction for seismic events was discussed in detail in [ 11 ], where temporal, spectral and cepstral features and combinations thereof are derived from the raw denoised measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Feature construction for seismic events was discussed in detail in [ 11 ], where temporal, spectral and cepstral features and combinations thereof are derived from the raw denoised measurements. Feature extraction [ 4 ] and selection [ 2 ] are commonly used for dimensionality reduction of the feature space, thus decreasing required storage requirements, and testing and training times of the classifier.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Stanković

Stankovic

et al. 2022

Sensors

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Slope instabilities caused by heavy rainfall, man-made activity or earthquakes can be characterised by seismic events. To minimise mortality and infrastructure damage, a good understanding of seismic signal properties characterising slope failures is therefore crucial to classify seismic events recorded from continuous recordings effectively. However, there are limited contributions towards understanding the importance of feature selection for the classification of seismic signals from continuous noisy recordings from multiple channels/sensors. This paper first proposes a novel multi-channel event-detection scheme based on Neyman–Pearson lemma and Multi-channel Coherency Migration (MCM) on the stacked signal across multi-channels. Furthermore, this paper adapts graph-based feature weight optimisation as feature selection, exploiting the signal’s physical characteristics, to improve signal classification. Specifically, we alternatively optimise the feature weight and classification label with graph smoothness and semidefinite programming (SDP). Experimental results show that with expert interpretation, compared with the conventional short-time average/long-time average (STA/LTA) detection approach, our detection method identified 614 more seismic events in five days. Furthermore, feature selection, especially via graph-based feature weight optimisation, provides more focused feature sets with less than half of the original number of features, at the same time enhancing the classification performance; for example, with feature selection, the Graph Laplacian Regularisation classifier (GLR) raised the rockfall and slide quake sensitivities to 92% and 88% from 89% and 85%, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Stanković

Stankovic

et al. 2022

Sensors

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“…They have proved to be successful tools (as a second opinion) for analyzing data in various fields of study, including volcanic seismology. Some examples of MLC applications in the volcano seismic event classification context have been developed from supervised learning models such as artificial neural networks [3], [4], deep neural networks [5], [6], support vector machine (SVM) [7], [8], random forest [9] decision trees [10], Hidden Markov Model (HMM) [11], [12], evolutionary algorithms [13], [14] and Gaussian mixture models (GMM) [15] to other approaches based on unsupervised learning [16], [17] and semi-supervised learning [18].…”

Section: Introductionmentioning

confidence: 99%

ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano

Grijalva

Ramos

Pérez

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised Machine Learning models might be considered to automatize the analysis of data acquired by in situ monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms, and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that Generative Adversarial Networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Frechet Distance and later on we reconstruct the signals into time-domain to be finally evaluated with Frechet Inception Distance.

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“…Li et al [17] proposed a pattern recognition method of mine microseismic and blasting events based on wave fractal features. Venegas et al [18] combined filter-based feature selection methods and a Gaussian mixture model to classify seismic events from Cotopaxi volcano. These methods all classify records by their waveform features, thereby avoiding the disadvantages of the postprocessing schemes employed in source parameter-based methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

2020

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The identification of suspicious microseismic events is the first crucial step in processing microseismic data. In this paper, we present an automatic classification method based on a deep learning approach for classifying microseismic records in underground mines. A total of 35 commonly used features in the time and frequency domains were extracted from waveforms. To examine the discriminative ability of these features, a genetic algorithm (GA)-optimized correlation-based feature selection (CFS) method was applied. As a result, 11 features were selected to represent microseismic records. By dividing each microseismic record into 50 frames, an 11 × 50 feature matrix was utilized as the input. A convolutional neural network (CNN) with 35 layers was trained on 20,000 samples recorded at the Huangtupo Copper and Zinc Mine. There are 5 types of events: microseismic events, blasting, ore extraction, mechanical noise, and electromagnetic interference. The event type was correctly determined by the trained CNN classifier 98.2% of the time, outperforming traditional machine learning methods.

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Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano

Cited by 22 publications

References 33 publications

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano

Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

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