Automatic Detection On Micro-seismic Event Identification And Phase First Arrival In Low SNR Signal

Based on acoustic emission, seismology developed from the micro-seismic monitoring technology is a kind of human mining activities occurred in the small earthquake events occurred in the collection, observation, and then analyze the human activities on the underground rock formation and the impact of the scope of the Earth Physical exploration technology. Micro-seismic monitoring technology has been widely used in the impact of ground pressure, mine water inrush and other mine power disaster monitoring and early warning, and has made many research results. Microseismic effective event identification and phase picking are one of the core problems of microseismic monitoring technology. It is one of the most important data processing to identify the microseismic events from the mass vibration monitoring data and to obtain the accurate picking of the initial phase Step, but also the basis and conditions for the study of source location,the inversion of source parameters and the study of source mechanism.The microseismic data collected by the microseismic monitoring system are characterized by nonstationarity and diversity. The signal-to-noise ratio of the collected data is low due to the influence of various external factors such as mechanical shock, electromagnetic noise,rock rupture and blasting vibration. Conventional microseismic event identification and initial phase picking methods cannot effectively handle low signal to noise ratio signals. The study shows that the microseismic signal is more sparse in the time-frequency domain. Based on this, a new method of micro-seismic event identification and phase-picking is proposed, which is called S-AIC method. In this method,the time-frequency analysis is carried out by applying the wavelet transform to the acquired signal, and then the Renyi calculation function is used to measure the time-frequency analysis result. According to the entropy, it is necessary to determine whether there is a microseismic event in the signal. Secondly, the application of the monitoring data(AIC) algorithm is used to select the appropriate time window for the Akashi Information Criterion (AIC) algorithm. Finally, in the time window of the selected time window, the time-frequency method is used to calculate the time- The AIC algorithm is used to calculate the exact phase of the phase.In this paper, we use the program to experiment with the microseismic data. Through comparison, we find that the accuracy of the high SNR signal is higher than that in the range of 0-20ms. The accuracy of the method is high, Is 100%, and the recognition accuracy of the low SNR signal is 92%, which is superior to the conventional identification method. The AIC method and the S-AIC method were used to select the phase first arrival pick-up experiment of 100 microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio. The results were as follows: The accuracy of S-AIC method is 100% and takes 0.812 s for high signal-to-noise ratio (SNR) signal. The accuracy of picking with low SNR signal is 91% and the average time is 1.325 s.