Real time identification of microseismic events based on ridge regression improved normative variable analysis
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摘要: 微震事件判识是煤矿微震监测的基础。现有的微震监测技术大多基于单物理量变化规律而开发，在处理含有大量噪声和干扰信号的煤矿微震数据时易产生误判情况。针对该问题，基于岭回归算法改进规范变量分析（CVA）的损失函数，实现稀疏化建模，以提升模型泛化能力。采用岭回归改进CVA对多通道煤矿微震监测数据进行融合分析，进而实时判识复杂微震监测数据状态。采用模拟数据和实际煤矿微震监测数据对岭回归改进CVA进行实验验证。在基于模拟数据的实验中，随着噪声方差由5×10−6增大至5×10−2，岭回归改进CVA的判识准确率较CVA提升了0.6%～5.4%，误报率和漏报率之和较CVA下降4.8%～17.3%。在基于实际微震监测数据的实验中，岭回归改进CVA对20个通道的微震监测数据融合分析结果能够反映出微震信号处于波动状态，验证了该方法具备微震事件实时判识能力，平均判识准确率为97.14%，较CVA高2.39%，误报率与漏报率之和为31.06%，较CVA降低0.07%，错误率为2.86%，较CVA降低2.4%。Abstract: The identification of microseismic events is the foundation of microseismic monitoring in coal mines. Most existing microseismic monitoring technologies are developed based on the variation law of single physical quantities, which can easily lead to misjudgment when processing coal mine microseismic data containing a large amount of noise and interference signals. In order to solve the above problem, the loss function of canonical variate analysis (CVA) is optimized and improved using ridge regression algorithm to achieve sparse modeling and enhance the model's generalization capability. The ridge regression improved CVA is used for fusion analysis of multi-channel coal mine microseismic monitoring data, and then real-time identification of complex microseismic monitoring data status is achieved. The simulated data and actual coal mine microseismic monitoring data are used for experimental verification of ridge regression improved CVA. In experiments based on simulated data, as the noise variance increases from 5×10−6 to 5×10−2, the recognition accuracy of ridge regression improved CVA increases by 0.6%-5.4% compared to CVA, and the sum of false alarm rate and omission rate decreases by 4.8%-17.3% compared to CVA. In experiments based on actual microseismic monitoring data, ridge regression improved CVA can reflect the fluctuation of microseismic signals in the fusion analysis results of 20 channels of microseismic monitoring data. It verifies that this method has the capability to identify microseismic events in real-time. The average identification accuracy is 97.14%, which is 2.39% higher than CVA. The sum of false alarm rate and omission rate is 31.06%, which is 0.07% lower than CVA. The error rate is 2.86%, which is 2.4% lower than CVA.
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图 4 微震监测数据融合分析统计结果
Figure 4. Statistics of fusion analysis of monitored microseismic data
表 1 微震数据实验结果
Table 1. Experiment results by microseismic data
% 测试集 CAC RFP+RFN RE 岭回归
改进CVACVA 岭回归
改进CVACVA 岭回归
改进CVACVA 1 97.95 95.64 2.0 5 4.36 2.05 4.36 2 98.08 94.65 1.9 2 5.35 1.92 5.35 3 99.07 94.89 0.9 3 5.11 0.93 5.11 4 96.10 94.76 63.4 49.33 3.90 5.24 5 93.95 93.00 53.80 58.94 6.05 7.00 6 97.70 95.53 64.27 63.66 2.30 4.47 平均值 97.14 94.75 31.06 31.13 2.86 5.26 
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