A Time Picking Method for Microseismic Data Based on LLE and Improved PSO Clustering Algorithm

Abstract

Time picking is of great concern in the processing of microseismic data. However, the traditional method based on time/frequency domain cannot pick the first arrival time accurately in low signal-to-noise ratio. Besides, the traditional time picking methods which based on clustering are sensitive to selecting the initial clustering centers and easy to converge to local optimal value. To solve the above problems, we propose a time picking method for microseismic data based on locally linear embedding (LLE) and improved particle swarm optimization (PSO) clustering algorithm. First, the LLE algorithm can obtain the inherent characteristics and the rules hidden in high-dimensional data by calculating Euclidean distances and reconstruction weights between microseismic data points. The input is represented in a low-dimensional form. Then, the improved PSO clustering algorithm is used to select the optimal clustering centers from low-dimensional data through global search method. After that, the low-dimensional data can be classified into noise cluster and signal cluster by the K-means algorithm. Finally, the initial time of the signal cluster can be considered as the first arrival time of microseismic data. The experimental results show that accuracy of the proposed method is higher than that of the improved PSO clustering algorithm, Akaike information criterion method, and short- and long-time window ratio method (short-time window averaging/long-time window averaging).