Abstract
In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.
Highlights
The processing of microseismic monitoring data has confronted with difficulty in balancing accuracy and efficiency for a long time [1,2,3]
After analyzing the energy ratio algorithm, the high-order statistics method, and the minimum information criterion (AIC) method, Akram et al [16] found that different methods had their own advantages and disadvantages under different conditions, so they designed a set of parameter-optimized first-arrival picking methods for microseismic signals, namely, different parameters were assigned to different first-arrival picking methods according to the microseismic signal conditions
The results show that compared with the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low-signal-to-noise ratio (SNR) microseismic signals, achieving significantly higher accuracy and efficiency than the Short-Term Average to Long-Term Average (STA/LTA) algorithm, which is well-known for its high efficiency in traditional algorithms
Summary
The processing of microseismic monitoring data has confronted with difficulty in balancing accuracy and efficiency for a long time [1,2,3]. The cross-correlation and the least squares criterion were used to preprocess the original microseismic data to obtain a better time difference correction, and the multichannel semblance parameter was used to identify the microseismic events. Dowan et al [17] proposed a first-arrival picking method of noisy microseismic records by combining the cross-correlation and superposition process. Raj et al [19] proposed a first-arrival picking method of microseisms based on the twodimensional constant false alarm rate, which improved the first-arrival picking accuracy under the low-SNR conditions. Sheng et al [21] developed the Shearlet Transform-Short time window/Long time window-Kurtosis (S-S/L_K) algorithm using the Shearlet transform and high-order statistics, which could accurately pick the first arrival of low-SNR microseismic signals.
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