An Adaptive FCM-based Approach of First Arrival Time Picking for Microseismic Data

Accurate picking of first arrival time plays a critical role in event localization and further data processing in microseismic(MS) monitoring. A large amount of data from receivers make effective automatic time picking method an urgent issue. In this letter, we proposed an adaptive automated time picking approach based on fuzzy c-means (FCM) clustering algorithm. First, by applying FCM, data points are assigned to two clusters with certain membership degrees: signal cluster and noise cluster. Then the vector describing data points to signal cluster center is extracted from the membership degree matrix. Second, considering the shortcoming of a preset threshold, correlation coefficient based adaptive selection algorithm is performed to obtain an optimal threshold for MS event picking. Finally, tests on the synthesis and real data illustrate that our approach outperforms the short-term and long-term average ratio (SLTA) and Akaike information criterion (AIC), and it is more robust than the traditional FCM-based picking method.