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首页> 外文会议>International Geoscience and Remote Sensing Symposium >IMPROVING FEATURE EXTRACTION IN THE AUTOMATIC CLASSIFICATION OF SEISMIC EVENTS. APPLICATION TO COLIMA AND ARENAL VOLCANOES
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IMPROVING FEATURE EXTRACTION IN THE AUTOMATIC CLASSIFICATION OF SEISMIC EVENTS. APPLICATION TO COLIMA AND ARENAL VOLCANOES

机译：改善地震事件自动分类中的特征提取。在科利马和阿雷纳火山的应用

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摘要

Monitoring of precursory seismicity in volcanoes is the most reliable and widely used technique in volcano monitoring. Since a visual inspection by human operators is a tedious task in a non-stop monitoring process, Hidden Markov Models have been previously proposed to automatically classify the different types of volcano-seismic events. Mel Frequency Cepstral Coefficients were successfully used as feature vector in this continuous classification system. In this paper seven novel features to be included in the MFCC feature vector are proposed. A very elementary GMM-based classifier has been implemented in order to assess the efficiency of the proposed parameters. Results using hundreds of events recorded from stations situated at Colima (Mexico) and Arenal (Costa Rica) volcanoes show that the proposed features improve the recognition accuracy and therefore they may be relevant in continuous volcano-seismic event automatic classification.
机译：在火山中监测火山中的前兆地震性是火山监测中最可靠和最广泛的技术。由于人类运营商的目视检查是在不间断监测过程中的繁琐任务，因此先前已经提出隐藏的马尔可夫模型自动分类不同类型的火山地震事件。在该连续分类系统中成功地使用MEL频率抗剖力系数作为特征向量。在本文中，提出了七种要包含在MFCC特征向量中的新功能。已经实现了基于GMM的基于GMM的分类器，以评估所提出的参数的效率。结果使用位于科尔马（墨西哥）和阿雷纳（CostaRica）的站点中录制的数百个事件（Costa Rica）火山，表明，提出的功能提高了识别准确性，因此它们可能与连续火山地震事件自动分类相关。

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