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• W3000045246 abstract "The identification of suspicious microseismic events is the first crucial step in processing microseismic data. In this paper, we present an automatic classification method based on a deep learning approach for classifying microseismic records in underground mines. A total of 35 commonly used features in the time and frequency domains were extracted from waveforms. To examine the discriminative ability of these features, a genetic algorithm (GA)-optimized correlation-based feature selection (CFS) method was applied. As a result, 11 features were selected to represent microseismic records. By dividing each microseismic record into 50 frames, an 11 × 50 feature matrix was utilized as the input. A convolutional neural network (CNN) with 35 layers was trained on 20,000 samples recorded at the Huangtupo Copper and Zinc Mine. There are 5 types of events: microseismic events, blasting, ore extraction, mechanical noise, and electromagnetic interference. The event type was correctly determined by the trained CNN classifier 98.2% of the time, outperforming traditional machine learning methods." @default.
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• W3000045246 date "2020-01-01" @default.
• W3000045246 modified "2023-10-09" @default.
• W3000045246 title "Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach" @default.
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• W3000045246 doi "https://doi.org/10.1109/access.2020.2967121" @default.
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