Microseismic Hypocenter Location Using an Artificial Neural Network

The sharp increase in the occurrence of human induced earthquakes globally requires real-time source location capabilities, particularly in areas where no prior seismic activity occurred. Recent advances in the field of machine learning coupled with available computational resources provide a great opportunity to address the challenge. Researchers have started looking into using convolutional neural networks (CNNs) for hypocenter determination by training on already located seismic events. We propose an alternate approach to the problem. We train a feed-forward neural network on synthetic P-wave arrival time data (based on a velocity model or empirical data). Once trained, the neural network can be deployed for real-time location of seismic events using observed P-wave arrival times. The use of a feed-forward neural network allows fast training compared to CNNs. We show sensitivity of the proposed method to the training dataset (density and distribution of the training sources), noise in the arrival times of the detected events, and size of the monitoring network.