Time and Frequency Feature Selection for Seismic Events from Cotopaxi Volcano

Lara-Cueva, Román; Bernal, Paúl; Saltos, Maria Gabriela; Benítez, Diego S.; Rojo‐Álvarez, José Luis

doi:10.1109/apcase.2015.30

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…The selection of features was based on a state-of-the-art study, drawing from the suggestions made in studies such as [17,[20][21][22]26] to aggregate contributions of information from various domains, such as the temporal, frequency, and scale domains. Conversely, feature reduction was proven to be an effective method for training neural networks [26].…”

Section: Volcanic Seismic Event Detection 221 Data Curation and Enric...mentioning

confidence: 99%

“…Lara et al [21] presented feature selection for seismic events produced by the Cotopaxi Volcano. The time and frequency domains were employed to process the seismic signals.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the wavelet transform, different levels were extracted from the filtered signal (in the 0.5-17 Hz frequency band). The optimal parameters for seismic signal analysis using WT included selecting Daubechies 10 (DB10) as the mother wave and a maximum decomposition level of six [21]. These levels were selected considering the trade-off between the computational cost and the fact that each decomposition served as a filter through which the initial signal needed to pass [21].…”

mentioning

confidence: 99%

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Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Sosa,

Molina,

Spagnotto

et al. 2024

Electronics

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This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.

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Section: Volcanic Seismic Event Detection 221 Data Curation and Enric...mentioning

confidence: 99%

“…Lara et al [21] presented feature selection for seismic events produced by the Cotopaxi Volcano. The time and frequency domains were employed to process the seismic signals.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Sosa,

Molina,

Spagnotto

et al. 2024

Electronics

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“…We then extract the energy of the signal and the zero crossing density #of Zero Crossing Points #of Total Sample Points [19] for acoustic feature extraction. We also extract the skewness and the kurtosis [20] in addition to energy and zero crossing density for seismic feature extraction. In addition to these features, we extract peak-to-peak value as a feature.…”

Section: Time-domain Feature Extractionmentioning

confidence: 99%

A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

Köse¹,

Hocaoglu²

2019

Turk J Elec Eng & Comp Sci

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Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic sensors and showed that the classification performance varies with the type of road due to the distinct spectral characteristics of the medium. Our proposed classifier fuses time and frequency-domain features and acoustic and seismic modalities to achieve the highest classification rate of 98.6% using a relatively small number of features.

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“…In this case, the number of parameters used as features were 80 (see [38] and references therein for details), and they were compiled as follows.…”

Section: (C) and (D) (B) Features From Signal Parameters In Several mentioning

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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Time and Frequency Feature Selection for Seismic Events from Cotopaxi Volcano

Cited by 13 publications

References 8 publications

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

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

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