1. Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory,Southwest University of Science and Technology,Mianyang 621010,China 2. Sichuan University of Science & Engineering,Zigong 643002,China; 3. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China 4. College of Computer Science and Technology,Southwest University of Science and Technology,Mianyang 621010,China
Accurate first-arrival picking plays a significant role in microseismic data processing.The dominate methods,i.e.,short-term average to long-term average ratio (STA/LTA) and the autoregressive (AR) model using Akaike Information Criterion (AIC) algorithm,are not optimal for picking up high-noise data.In order to optimize the first-arrival point estimation of high-noise data,the authors propose an improved picking method based on wavelet multi-scale analysis (WMA) and AIC algorithm.In this algorithm,WMA is used to decompose the high-noise three-component (3C) microseismic data,and the actual calculation data are reconstructed based on the approximation data.Then the maximum value of the absolute value is calculated to constrain the AIC calculation data segment;on such a basis,the global minimum value of the AIC sequence is selected as its first arrival point.The improved algorithm is verified by both synthetic data and field data in this paper.The results show that the improved first-arrival picking algorithm can be effectively applied to high-noise three component microseismic data processing and greatly enhance its accuracy(the error range is 0.25~0.5 ms).
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