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Appl. Sci. 2018, 8(5), 792; https://doi.org/10.3390/app8050792

Automatic Selection of Low-Permeability Sandstone Acoustic Emission Feature Parameters and Its Application in Moisture Identification

Key Laboratory for Optoelectronic Technology and System of the Education Ministry of China, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China
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Received: 24 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
(This article belongs to the Special Issue Structural Damage Detection and Health Monitoring)
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Abstract

Moisture is a vital factor in the structural stability of sandstone, which is the main component of low-permeability reservoir rocks. Hence, studies into moisture identification are crucial. Diverse information about rock, such as its structural and mechanical parameters, can be obtained from the acoustic emission (AE) signal. However, the types of AE parameters are varied, and the rock information that is represented by them is different. Traditional methods of parameter selection are mostly based on the correlation between parameters and the experience of researchers, which are not accurate when the correlation between parameters is fuzzy and does not meet automation requirements. In this study, a method of signal feature selection based on a data fluctuation rule and clustering analysis is proposed. This method takes the fluctuation law of the signal itself and the correlation degree of cluster labels as the basis, and the selection step is divided into two steps. An experimental platform is established, and uniaxial compression on sandstones with different moisture contents is carried out to verify the efficiency of this method. The selected feature parameters are used for moisture classification combined with a support vector machine (SVM) classifier, and the identification results verify the efficiency of energy security monitoring in low-permeability rocks. View Full-Text
Keywords: feature select; acoustic emission; fluctuation law; clustering analysis; rock moisture; support vector machines feature select; acoustic emission; fluctuation law; clustering analysis; rock moisture; support vector machines
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Tao, K.; Zheng, W. Automatic Selection of Low-Permeability Sandstone Acoustic Emission Feature Parameters and Its Application in Moisture Identification. Appl. Sci. 2018, 8, 792.

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