A New Volcanic Seismic Signal Descriptor and its Application to a Data Set From the Cotopaxi Volcano

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

doi:10.1109/tgrs.2020.2976896

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…According to the number of samples per species, the selected MLCs could provide reasonable classification performance without incurring the statistical assumption of not having the minimum required instances per sample or any overfitting during the models training process such as on artificial neural networks. A brief description of the employed classification algorithms are presented below: The naive Bayes (NB) classifier is based on probabilistic models with strong (naive) independence assumptions [ 32 ]. After training the NB classifier by estimating the class priors and the probability distribution of features, any test sample will follow the decision rule that provides the most probable value of the maximum a posteriori of the model.…”

Section: Methodsmentioning

confidence: 99%

“…The naive Bayes (NB) classifier is based on probabilistic models with strong (naive) independence assumptions [ 32 ]. After training the NB classifier by estimating the class priors and the probability distribution of features, any test sample will follow the decision rule that provides the most probable value of the maximum a posteriori of the model.…”

Section: Methodsmentioning

confidence: 99%

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An approach to automatic classification of Culicoides species by learning the wing morphology

et al. 2020

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Fast and accurate identification of biting midges is crucial in the study of Culicoides -borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify Culicoides species. A database containing wing images of C. obsoletus , C. pusillus , C. foxi , and C. insignis species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between C. obsoletus and C. pusillus species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between C. foxi and C. insignis species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An approach to automatic classification of Culicoides species by learning the wing morphology

et al. 2020

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“…In probability theory and statistics, skewness (Sk) is a measure of the variable's asymmetry to its mean. It is a shape parameter that characterizes the degree of asymmetry [28,29]. Skewness can be positive, negative, and zero.…”

Section: Statistical Featuresmentioning

confidence: 99%

Microseismic Signal Classification Based on Artificial Neural Networks

Xin

Jiang

Jin

2021

Shock and Vibration

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The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.

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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%

“…Traditional supervised learning models require a certain amount of labeled data for adequately learning the feature space during the training stage and then classify unseen data [9], [18]. However, the number of samples to train and test state-of-the-art MLCs as deep-learning models is a big concern due to data limitations such as insufficient amount of highquality instances in the training data, missing labels, and the imbalanced representation of classes, among others.…”

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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A New Volcanic Seismic Signal Descriptor and its Application to a Data Set From the Cotopaxi Volcano

Cited by 18 publications

References 25 publications

An approach to automatic classification of Culicoides species by learning the wing morphology

An approach to automatic classification of Culicoides species by learning the wing morphology

Microseismic Signal Classification Based on Artificial Neural Networks

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

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