Abstract: For the purpose of improving the accuracy and automation of first arrival picking method, the weighted K-means clustering algorithm is introduced. Firstly, various seismicattributes such as root-mean-squares amplitude, adjacent trace correlation, line integral and dominant frequency of amplitude spectrum are extracted. Then, weighted K-means clustering is performed for seismic attributes to identify the first arrival time window automatically.Finally, combined with phase correction method, this method is applied to realize the pickup of the first arrival time in the time window. The validity and feasibility of the proposed method, which is compared with STA/LTA and BP neural network, are verified by theoretical and practical data tests. The results suggest that the multi-attribute first arrival picking method based on weighted K-means clustering can pick up the first arrival of seismic data with low signal-to-noise ratios quickly and accurately, and enhance the automation of arrival time picking without the artificial identification of time window.