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Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

基于卷积神经网络与原始波形的微震与爆破事件辨识方法

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Abstract

Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring. The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass. The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology. An image identification model based on Convolutional Neural Network (CNN) is established in this paper for the seismic waveforms of microseismic events and blasts. Firstly, the training set, test set, and validation set are collected, which are composed of 5250, 1500, and 750 seismic waveforms of microseismic events and blasts, respectively. The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training. Results show that the accuracies of microseismic events and blasts are 99.46% and 99.33% in the test set, respectively. The accuracies of microseismic events and blasts are 100% and 98.13% in the validation set, respectively. The proposed method gives superior performance when compared with existed methods. The accuracies of models using logistic regression and artificial neural network (ANN) based on the same data set are 54.43% and 67.9% in the test set, respectively. Then, the ROC curves of the three models are obtained and compared, which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model. It not only decreases the influence of individual differences in experience, but also removes the errors induced by source and waveform parameters. It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.

摘要

微震监测系统作为深部矿山地压监测的有效手段之一,其作用原理是分析微震事件包含的力学 参数,为岩体稳定性分析提供最准确的信息。准确地辨识微震事件与爆破事件决定了微震监测技术预 警的时效性与准确性。鉴于微震事件和爆破事件的地震波形具有不同的特征,本文建立了一种基于卷 积神经网络的微震事件和爆破事件辨识模型。首先将采集到的微震与爆破事件波形分别组成训练 集(微震爆破事件各5250 个)、测试集(微震爆破事件各1500 个)以及验证集(无标签的微震爆破事件各 750 个),将分类得到的数据集进行预处理并在CPU 模式下输入至构建好的卷积神经网络模型中进行训 练。结果显示训练集中微震事件识别的准确率为99.46%,爆破事件识别的准确率为99.33%,验证集 中微震事件识别的准确率为100%,爆破事件识别的准确率为98.13%。与其他机器学习方法进行对比, 该方法拥有较高的辨识准确率。逻辑回归模型与人工神经网络模型在相同测试集下的准确率仅为 54.43%和67.90%。通过绘制并对比三种模型的ROC 曲线,可以看出由于使用原始波形训练模型,CNN 在辨识微震与爆破事件中表现出了绝对的优势。这不仅减少了个体经验差异的影响,而且消除了提取 震源参数和波形参数过程中产生的误差。证明了本文所建立的微震与爆破事件辨识方法提高了微震数 据处理的速度和精度。

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Long-jun Dong  (董陇军), Zheng Tang  (唐正), Xi-bing Li  (李夕兵) & Yong-chao Chen  (陈永超)

  2. School of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang, 330013, China

    Jin-chun Xue  (薛锦春)

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  1. Long-jun Dong  (董陇军)
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Correspondence to Jin-chun Xue  (薛锦春).

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DONG Long-jun, TANG Zheng, LI Xi-bing, CHEN Yong-chao, and XUE Jin-chun declare that they have no conflict of interest.

Foundation item: Projects(51822407, 51774327, 51664016) supported by the National Natural Science Foundation of China

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Dong, Lj., Tang, Z., Li, Xb. et al. Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform. J. Cent. South Univ. Technol. 27, 3078–3089 (2020). https://doi.org/10.1007/s11771-020-4530-8

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