Arrival-time picking of microseismic events based on MSNet

Abstract

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, because field monitoring data contain many microseismic events with low signal-to-noise ratios (S/Ns), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-S/N environments. Therefore, we have adopted a deep convolutional network that combines the U-Net and DenseNet approaches to pick arrival times automatically. This novel network called MSNet not only retains the spatial information of any input signal or profile based on the U-Net, but also extracts and integrates more essential features of events and nonevents through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of our method. First, we describe the structure of MSNet and the workflow of our picking method. Then, data sets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different S/Ns and field microseismic data from different monitoring areas. By comparing the picking results of our method with the results of U-Net and short-term average and long-term average methods, the effectiveness of our method is verified. The arrival-picking results of synthetic data and microseismic field data indicate that our network has increased adaptability and can achieve high accuracy for picking the arrival time of microseismic events.