Abstract Man-made activities contribute to the ambient seismic wavefield. Locating these anthropogenic seismic noise sources at a high spatiotemporal resolution becomes vital for identifying human activities, understanding source mechanisms, and imaging subsurface geological structures. Existing noise source localization methods based on array analysis face challenges in time-lapse monitoring of the source distribution due to the computational cost. Here, we introduce an efficient approach of using deep learning to monitor the change of seismic noise source locations. The neural network extracts noise source locations from noise cross-correlation functions. The network model is trained with abundant simulated data at a low cost, and then it is sustained for monitoring the changes of noise source distribution in a survey area. We propose a comprehensive framework to ensure prediction accuracy and stability, which allows us to monitor the noise source distribution at a high spatiotemporal resolution. Field data examples acquired by small nodal arrays in the urban area of Hangzhou, China, suggest that the anthropogenic noise sources can be monitored at meter and second scales. The results reveal spatiotemporal variations of the vehicle traffic distribution along a road and two drilling machines working at a construction site. Our study demonstrates an approach to develop deep learning models for real-time monitoring of the distribution of seismic noise sources associated with human activities.
Zhou et al. (Wed,) studied this question.