Building Machine Learning Models for Long-Period and Volcano-Tectonic Event Classification

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

doi:10.1109/chilecon47746.2019.8987505

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Note that RF has lower complexity than MLP. In [45], to classify long-period and volcano-tectonic signals, five classifiers: Naive Bayes (NB), SVM, K-Nearest Neighbors (KNN), feed-forward backpropagation neural network (FFBP) and RF were implemented with 84 features; RF performed the best, following by NB and KNN classifiers with FFBP neural network slightly worse. The SVM classifier did not perform well because it was implemented with a linear kernel only.…”

Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

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Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, non site-specific feature construction, feature selection and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field datasets and benchmarked against the state of the art.

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Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

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“…• The random forest (RF) is an ensemble algorithm based on the combination of several empirical selected tree predictors and an average function for deciding the final instance classification [36]. F 2 = Z Z is the number of segmented zones.…”

Section: Plos Onementioning

confidence: 99%

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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“…5) Classification: a supervised learning problem may be viewed as a problem of data separation into different classes, i.e., the output value of a prediction function after the classifier has been trained using several input-output valid pairs [23]. The classification of Culicoides species is a problem that involves two discrete output classes: Pusillus and Obsoletus species.…”

Section: A Automatic Culicoides Species Classificationmentioning

confidence: 99%

MosCla app: An android app to classify Culicoides species

Gutierrez

Pérez

Benítez

et al. 2020

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)

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Building Machine Learning Models for Long-Period and Volcano-Tectonic Event Classification

Cited by 11 publications

References 21 publications

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

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

MosCla app: An android app to classify Culicoides species

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