Automatic First Arrival Time Identification Using Fuzzy C-Means and AIC

Accurate first arrival picking plays a crucial role in microseismic data processing. However, it is challenging to guarantee satisfactory accuracy with conventional approaches when the signal-to-noise ratio (SNR) of data is low. This article proposes an automatic first arrival time picking method based on fuzzy $C$ means clustering (FCM) and Akaike information criterion (AIC). The proposed method consists of three steps: clustering, rough picking, and adjusting. First, we employ FCM to divide each data point into the signal cluster and the noise cluster according to a fuzzy partition. Second, unlike conventional FCM-based picking approaches, we utilize Otsu’s method to determine a data-dependent threshold, instead of an artificially predefined one, to obtain a coarse result of the microseismic event interval from clustering partition. Finally, note that the microseismic event data points are concentrated in amplitude and also correlated in time. Therefore, we employ the AIC of the clustering partition to seek time-varying information to adjust the coarse result. Besides, we investigated several commonly used characteristic factors to introduce a supervised guideline for feature selection in first arrival picking with FCM. At last, we carried out simulations and real field data tests to verify the reliability of the proposed method. The experimental results demonstrate that the proposed method outperforms the short-and long-time average ratio (SLTA) method, the AIC method, and the conventional FCM-based picking method.