Deep-Learning for Volcanic Seismic Events Classification

Salazar, Aaron; Arroyo, Rodrigo; Pérez, Noel; Benítez, Diego S.

doi:10.1109/colcaci50549.2020.9247848

Cited by 7 publications

(1 citation statement)

References 27 publications

(25 reference statements)

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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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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

Self Cite

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On the Use of Convolutional Neural Network Architectures for Facial Emotion Recognition

Espinel

Pérez

Riofrío

et al. 2022

Applications of Computational Intelligence

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Discrimination between icequakes and earthquakes in southern Alaska: an exploration of waveform features using Random Forest algorithm

Kharita,

Denolle,

West

2024

Geophysical Journal International

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Summary This study examines the feature space of seismic waveforms often used in machine learning applications for seismic event detection and classification problems. Our investigation centers on the southern Alaska region, where the seismic record captures diverse seismic activity, notably from the calving of marine-terminating glaciers and tectonic earthquakes along active plate boundaries. While the automated discrimination of earthquakes and glacier quakes is our nominal goal, this dataset provides an outstanding opportunity to explore the general feature space of regional seismic phases. That objective has applicability beyond ice quakes and our geographic region of study. We make a noteworthy discovery that features rooted in the spectral content of seismic waveforms consistently outperform statistical and temporal features. Spectral features demonstrate robust performance, exhibiting resilience to class imbalance while being minimally impacted by factors such as epicentral distance and signal-to-noise ratio. We also conduct experiments on the transferability of the model and find that transferability primarily depends on the appearance of the waveforms. Finally, we analyze misclassified events and find examples that are identified incorrectly in the original regional catalog.

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Deep-Learning for Volcanic Seismic Events Classification

Cited by 7 publications

References 27 publications

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

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

On the Use of Convolutional Neural Network Architectures for Facial Emotion Recognition

Discrimination between icequakes and earthquakes in southern Alaska: an exploration of waveform features using Random Forest algorithm

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