期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 12, 期 6, 页码 1991-2003出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2019.2916045
关键词
Feature selection methods; Gaussian mixture model (GMM) classifier; redundancy analysis; seismic events classification
类别
资金
- Universidad San Francisco de Quito USFQ (Poligrants Program) [10100, 12494]
- Universidad de las Fuerzas Armadas ESPE [2013-PIT-014, 2016-EXT-038]
This paper proposes an exhaustive evaluation of five different filter-based feature selectionmethods in combination with a Gaussian mixture model classifier for the classification of long-period (LP) and volcano-tectonic (VT) seismic events recorded at Cotopaxi volcano in Ecuador. The experimentation included both exploring and ranking search spaces of seismic-signal-based features, and selecting subsets of optimal features for event classification. The evaluation was carried out by using an experimental dataset formed by 587 LP and 81 VT feature vectors, each composed of 84 statistical, temporal, spectral, and scale-domain features extracted from the original seismic signals. The best result in accuracy, precision, recall, and processing time for LP seismic event classification was obtained by using the Chi2 discretization method with five features, achieving 95.62%, 99.08%, 95.94%, and 3.7 ms, respectively, whereas for VT seismic event classification, the uFilter method with five features reached the scores of 96.71%, 85.23%, 96.00%, and 4.1 ms, respectively. For the classification of both seismic events simultaneously, the uFilter method with five features yielded 96.70%, 97.77%, 96.7%, and 4.1 ms, respectively. According to the Wilcoxon statistical test, these classification schemes provide competitive seismic event classification, while reducing the required processing time.
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