A Time Picking Method for Microseismic Data Based on LLE and Improved PSO Clustering Algorithm
A Time Picking Method for Microseismic Data Based on LLE and Improved PSO Clustering Algorithm
复制标题
基于LLE和改进PSO聚类算法的微震数据时间选取方法
DOI：
10.1109/lgrs.2018.2854834
复制
发表时间：
2018-11-01
影响因子：
4.8
通讯作者：
Meng, Yuqi
中科院分区：
文献类型：
--
作者：
Ma, Haitao;Wang, Teng;Meng, Yuqi
时间拾取是微震数据处理中备受关注的问题。然而，传统的基于时频域的方法在低信噪比情况下无法准确拾取首到达时间。此外，传统的基于聚类的时间选取方法对初始聚类中心的选择比较敏感，并且容易收敛到局部最优值。为了解决上述问题，我们提出了一种基于局部线性嵌入（LLE）和改进的粒子群优化（PSO）聚类算法的微震数据时间拾取方法。首先，LLE算法通过计算微震数据点之间的欧氏距离和重建权重，可以获得高维数据的内在特征和隐藏的规则。输入以低维形式表示。然后，采用改进的PSO聚类算法，通过全局搜索方法从低维数据中选择最优聚类中心。之后，通过K-means算法可以将低维数据分为噪声簇和信号簇。最后，信号簇的初始时间可以视为微震数据的初至时间。实验结果表明，该方法的准确率高于改进PSO聚类算法、Akaike信息准则法、短时窗比法（短时窗平均/长时窗平均）。
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).