Multi-trace joint downhole microseismic phase detection and arrival picking method based on U-Net

Microseismic phase identification and arrival picking are two key steps in the data processing of hydraulic fracturing microseismic monitoring, the results of which have great influences on the subsequent event location and the interpretation of hydraulic fractures. To complete the phase identification and arrival picking, conventional methods (such as STA/LTA method, template matching method, multi-trace cross-correlation method, etc.) need to extract characteristic differences between valid signals and noise in amplitude, polarization, frequency, and waveform similarity. Based on the automatic characteristic extraction capability of deep learning technology, considering the characteristics of multi-trace data sources in the downhole microseismic observation system, this paper has proposed a U-Net-based multi-trace joint microseismic phase detection and arrival picking method (MT-Net). Our method adopts the U-Net model, which has the "point-by-point" detection ability. In the model training stage, multi-trace microseismic monitoring records with different signal characteristics are input to the model, the probability distribution labels of P waves, S waves and noises are used as model output. The two-dimensional convolution operation enables the "in-trace" and "inter-trace" waveform information to be adaptively learned meanwhile, achieving the goal of high consistency in the processing results between waveform records of adjacent traces. In the model testing stage, the segmented waveforms in the continuous record are fed into the model, and the waveform classification among single-phases, double-phases and noises is completed by setting the probability distribution thresholds of P waves and S waves. Meanwhile, the arrival picking of valid microseismic events is completed. The processing results of actual microseismic data show that compared with the single-trace method similarly based on U-Net (ST-Net), our method significantly reduces the probability of missed and mistaken events with low signal-to-noise ratio in the phase detection. Also, serious deviations of the picking results on some single traces and the false pickings between the P and S phases are effectively avoided by our method in the arrival picking. The overall picking result of our method has reached a level close to that of the multi-trace cross-correlation method, which can meet the requirements of real-time and accuracy in the microseismic monitoring data processing.