Detection and classification of seismic events with progressive multi-channel correlation and hidden Markov models

被引：25

作者：

Quang, Paul Bui ^{[1
]}

Gaillard, Pierre ^{[1
]}

Cano, Yoann ^{[1
]}

Ulzibat, Munkhuu ^{[2
]}

机构：

[1] CEA, DAM, DIF, F-91297 Arpajon, France

[2] Mongolian Acad Sci, RCAG, Ulaanbaatar, Mongolia

来源：

COMPUTERS & GEOSCIENCES | 2015年 / 83卷

关键词：

Hidden Markov model; Progressive multi-channel correlation; Pattern recognition; Regional seismicity; EARTHQUAKE DETECTION; SIGNALS; PHASE;

D O I：

10.1016/j.cageo.2015.07.002

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The progressive multi-channel correlation (PMCC) detector and a statistical time series classifier based on hidden Markov models (HMMs) are associated in order to detect and classify seismic events at an array station. We classify the signal patterns that have triggered PMCC detections thanks to HMMs. The signal is transformed into sequences of feature vectors, which are used to train HMMs. The features include outputs from the PMCC detector describing the seismic wave propagation. Our method is applied to the automatic classification of regional seismic event, teleseismic event, and noise. The experimental data set comprises records from the Songino array station in Mongolia. Results on test events show an average classification performance of 84%. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：110 / 119

页数：10

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