This paper proposes a novel approach for the rock collapse forecasting based on the automatic classification of micro-acoustic emissions in the wavelet domain. Solutions present in the literature are surpassed in two main directions. First, we designed a novel and comprehensive set of features extracted from micro-acoustic emissions based on the Discrete Wavelet Transform. Second, we consider and contrast several machine learning classification techniques. We evaluated the accuracy of the proposed approach on real-world data acquired by a real-time monitoring system for rock-collapse forecasting deployed in Northern Italy. Experimental results demonstrate the effectiveness of what proposed.
Rock collapse forecasting: A novel approach based on the classification of micro-acoustic signals in the wavelet domain / S. Ntalampiras, M. Roveri (PROCEEDINGS OF IEEE SENSORS ...). - In: SENSORS, 2013 IEEE[s.l] : IEEE, 2013. - ISBN 9781467346405. - pp. 1569-1572 (( Intervento presentato al 12. convegno IEEE Sensors Conference tenutosi a Baltimore nel 2013.
Rock collapse forecasting: A novel approach based on the classification of micro-acoustic signals in the wavelet domain
S. Ntalampiras;M. Roveri
2013
Abstract
This paper proposes a novel approach for the rock collapse forecasting based on the automatic classification of micro-acoustic emissions in the wavelet domain. Solutions present in the literature are surpassed in two main directions. First, we designed a novel and comprehensive set of features extracted from micro-acoustic emissions based on the Discrete Wavelet Transform. Second, we consider and contrast several machine learning classification techniques. We evaluated the accuracy of the proposed approach on real-world data acquired by a real-time monitoring system for rock-collapse forecasting deployed in Northern Italy. Experimental results demonstrate the effectiveness of what proposed.File | Dimensione | Formato | |
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