Quality control of microseismic P-phase arrival picks in coal mine based on machine learning

Microseismic events generally contain strong noise-polluting and unobvious P-phase oscillating channel waveforms. The automatic P-phase arrival picking accuracy of these channel waveforms tends to be low, or even are false. Currently, unusable P-picks are not screened out automatically before geophysics inversions in most microseismic data processing software. Therefore, manual interventions are needed to remove or correct the unusable P-picks. However, rapidly increasing monitoring data causes manual handling to be time-consuming and lagging. Supervised machine learning (ML) is applied to distinguish useable and unusable P-picks automatically. Big data analysis revealed that the waveform features, including signal-to-noise ratio, signal-to-noise variance ratio, P-wave starting-up slope, and peak amplitude have impact on P-pick accuracy. In contrast, the effect of the short-time zero-crossing rate on the P-pick accuracy is not as obvious. Five P-pick quality control models were trained based on traditional machine learning approaches, including discriminant analysis, logistic regression, k-nearest neighbor, support vector machine, and Naive Bayes classifier. For these five models, the input data are P-pick labels and waveform features. In addition, another P-pick quality control model was trained based on convolutional neural network. While, the input data are P-pick images and labels. The training sets used in all six machine learning models are uniform. The testing experiments with uniform testing set show that the support vector machine generated best the performance among traditional machine learning approaches, with 82.81% accuracy. However, the convolutional neural network model generated outstanding performance in recognizing P-pick, with 91.71% accuracy. The automatic P-pick quality control method proposed in this study can facilitate the precision and efficiency of the automatic processing of microseismic signals.

Automated summary

The study proposed an automatic quality control method for P-wave arrival time using supervised machine learning, which distinguishes usable and unusable P-picks based on waveform features. Experimental results showed that support vector machine performed the best among traditional machine learning approaches, while convolutional neural network model excelled in recognizing P-picks.