Abstract
The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability.
Highlights
The identification of suspicious microseismic events is the first crucial step in microseismic data processing
The classification of suspicious microseismic records depends on the visual scanning of waveforms by experienced analysts[11]
Dong et al.[19,20] proposed a discrimination method for seismic and blasting events based on a Fisher classifier, a naive Bayesian method and logistic regression; this method regards the logarithm of the seismic moment, the logarithm of the seismic energy, and the probability density function of the arrival time between adjacent sources as features
Summary
The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Dong et al.[19,20] proposed a discrimination method for seismic and blasting events based on a Fisher classifier, a naive Bayesian method and logistic regression; this method regards the logarithm of the seismic moment, the logarithm of the seismic energy, and the probability density function of the arrival time between adjacent sources as features These researches promote the research process in this field, it still cannot realize the automatic identification of complex microseismic records in the actual production process. We propose an approach to establish an automatic classifier for multi-class microseismic records with limited samples using the Capsule Network (CapsNet) This approach allows most of the current mines, both old and new, to use deep learning as early as possible to achieve the automatic classification of microseismic records and has a reliable result. The proposed method will be applied to field datasets to demonstrate the efficiency and reliability of the classification of limited microseismic data
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