First arrival time picking algorithm of micro-seismic based on improved STA/LTA and adaptive VMD
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摘要: 针对低信噪比条件下微震初至拾取准确度低的问题，基于信号幅度变化引入权重因子，对传统长短时窗比值（STA/LTA）算法进行改进，提高初次拾取精度。为了进一步降低拾取误差，对变分模态分解（VMD）算法进行优化，基于互相关系数和排列熵准则自适应确定VMD分解层数，对初次拾取结果前后2—3 s的记录进行优化VMD，并计算分解后各本征模函数（IMF）的峰度赤池信息准则值，得到各IMF的到时，以各IMF的拾取结果及能量比综合加权得到二次拾取到时。仿真实验表明：改进后的STA/LTA在较低信噪比下可降低初次拾取误差约0.01 s以上；相比经验模态分解（EMD)和小波包分解，自适应VMD分解后能再次降低误差，最终与人工拾取结果平均误差在0.023 s以内。实际微震信号初至拾取结果表明，本算法能快速有效地识别初至P波，与人工拾取结果相比误差小，准确率高。Abstract: Accurate and reliable picking of the first arrival time is one of the critical steps in micro-seismic monitoring. Aiming at the problem of low accuracy of first arrival picking for micro-seisms under low signal-to-noise ratio, the traditional short term averaging/long term averaging algorithm is improved by introducing weight factor according to the change of signal amplitude to improve the accuracy of initial pickup. In order to further reduce the pickup error, variational mode decomposition （VMD） is optimized based on cross-correlation coefficient and permutation entropy criterion, and decomposition layers are determined adaptively. Then, the signals of 2−3 s before and after the initial pickup are decomposed by VMD, and the Kurtosis-Akaike information criterion （AIC） values of the decomposed intrinsic mode functions （IMF） are calculated to get the arrival time of each IMF, and the secondary arrival time is obtained by comprehensively weighting the picking results and energy ratios of each IMF. Simulation results show that the improved STA/LTA can reduce the initial picking error by more than 0.01 s at low SNR; compared with empirical mode decomposition （EMD） and wavelet packet decomposition, the adaptive VMD decomposition can reduce the picking error again, and the finalaverage picking error is less than 0.023 s. The first arrival time picking results of real micro-seismic signals show that the proposed algorithm can identify the first break of P-wave quickly and effectively, and the error is smaller than that of manual picking, which shows that the algorithm is effective and the picking accuracy is high.
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Key words:
- STA/LTA /
- variational mode decomposition /
- first arrival picking /
- micro-seismic /
- AIC
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图 1 基于改进STA/LTA及优化VMD的微震初至拾取算法流程图
Figure 1. The flow chart of the first break picking of micro-seismic based on imp-roved STA/LTA and adaptive VMD
图 2 基于雷克子波和卷积模型的理论和模拟地震记录
Figure 2. Theoretical and simulated seismogram based on Ricker wavelet and convolution model
图 4 不同SNR下三种STA/LTA算法拾取误差曲线
Figure 4. Picking up error curves of three STA/LTA algorithms under different SNRs
图 5 优化VMD峰度AIC为3.07 s时的初至拾取结果
Figure 5. First arrival picking based on improved VMD when Kurtosis-AIC is 3.07 s
图 7 基于小波包峰度AIC的初至拾取
Figure 7. First arrival picking based on wavelet packet Kurtosis-AIC
图 10 广西平果2013年7月4日ML1.3爆破的一条实际微震记录
Figure 10. Real micro-seismic record of some coal mine with ML1.3 at Pingguo，Guangxi on July 4th 2013
图 11 本文改进STA/LTA初次拾取结果
Figure 11. First picking results of improved STA/LTA in this paper
图 12 本文改进STA/LTA及优化VMD峰度AIC拾取结果
Figure 12. First arrival picking by improved STA/LTA and adaptive VMD Kurtosis-AIC
表 1 实际微震记录初至拾取结果
Table 1. First arrival picking of real micro-seismics
拾取方法 误差 30 ms 误差 20 ms 误差 10 ms 记录条数 占比 记录条数 占比 记录条数 占比 本文 1 117 98.2% 1 090 95.9% 1 031 90.7% EMD-AIC 1 090 95.9% 1 052 92.5% 980 86.2% 小波包峰度AIC 1 075 94.5% 1 037 91.2% 968 85.1% 
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