An FCM-based microseismic phase arrival picking method and application

Artificial intelligence-based methods for picking microseismic phase arrivals have been widely adopted. However, these methods are frequently challenged by complex and dynamic monitoring scenarios, where various types of environmental noise mask low-energy microseismic signals. Moreover, the paucity of labelled data often impairs the reliability and accuracy of their results. To address these issues, this study proposes a novel supervised learning framework named FC-Net, which integrates automatic labelling via Fuzzy C-means clustering (FCM) with the U-Net architecture. Specifically, the FCM algorithm is employed to derive the probabilistic distributions of microseismic phase arrival times, which are then used as training labels for model training. The proposed FC-Net is equipped with soft attention gates (AGs) and recurrent-residual convolution units (RRCUs), which effectively enhance the network’s ability to focus on key seismic features. The arrival time is determined as the moment when the predicted probability exceeds a predefined threshold for the first arrival pick. Evaluated on a field dataset collected from Southwest China, FC-Net is demonstrated to outperform the conventional U-Net method. The experimental results demonstrate that FC-Net achieves adaptive labeling, enhances the detection rate of microseismic events, and improves the precision of phase arrival picking. Furthermore, it exhibits strong generalization performance across microseismic events with varying signal-to-noise ratios (SNRs).