Time Series Segmentation Clustering: A New Method for S-Phase Picking in Microseismic Data

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS(2022)

引用 3|浏览1
暂无评分
摘要
Phase picking is a critical step in the analysis of microseismic data. However, there is a lack of relevant methods for S-phase picking. In this letter, we pick the P-phase and extract the data around the S-phase arrival firstly based on previous studies. Then, we propose a method called time series segmentation clustering (TSSC) which is based on the K-means algorithm to pick the S-phase. In the TSSC method, we construct a feature vector of the microseismic signal and standardize the feature vector first. Then, we modify the K-means objective function into a function of time. We subsequently obtain the S-phase arrival corresponding to the minimum value of the objective function. We tested the objective function of the TSSC method on synthetic signals and microseismic signals, and the results show that the method is reliable. This automatic picking algorithm has been used to pick 1763 S-phase arrivals from microseismic signals. In comparison with manual picking results, a statistical analysis shows that this method is feasible.
更多
查看译文
关键词
Data mining,Clustering algorithms,Monitoring,Time series analysis,Linear programming,Rocks,Partitioning algorithms,K-means algorithm,microseismic monitoring,phase arrival picking,S-wave
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要