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3D Microseismic Monitoring Using Machine Learning

Microseismic source localization is important for inferring the dynamic status of the subsurface stress field during hydraulic fracturing. Traditional deterministic methods for 3D microseismic source localization require either ray tracing or full waveform modeling, thus are computationally expensive. We propose a very efficient (e.g., within 1 s) microseismic source localization method based on machine learning. First, three‐dimensional (3D) ray tracing is performed with hypothetical event locations and realistic acquisition geometry to calculate the theoretical travel‐times. The theoretical travel‐time differences and the spatial locations of the stations are treated as the input features of the training data set, and the corresponding source locations are used as the labels. The manually or automatically picked arrival time differences between different stations and a reference station after a microseismic event and the actual station locations are fed into the well‐trained model for a fast and accurate location prediction. The proposed method is efficient enough to be widely applied for the real‐time monitoring of hydraulic fracturing. The machine learning model is analogous to 3D grid search but performs 3D ray tracings before the actual localization, and needs to be retrained when applied to a new study area. We use several synthetic tests and a real data example from the Weiyuan hydraulic fracturing experiment in Sichuan, China, to demonstrate the effectiveness of the proposed method. The application of the proposed method to earthquake localization is also demonstrated to be straightforward.