The important task of tracking seismic activity requires both sensitive detection and accurate earthquake location. Approximate earthquake locations can be estimated promptly and automatically; however, accurate locations depend on precise seismic phase picking, which is a laborious and time-consuming task. We adapted a deep neural network (DNN) phase picker trained on local seismic data to meso-scale hydraulic fracturing experiments. We designed a novel workflow, transfer-learning aided double-difference tomography, to overcome the three orders of magnitude difference in both spatial and temporal scales between our data and data used to train the original DNN. Only 3,500 seismograms (0.45% of the original DNN data) were needed to re-train the original DNN model successfully. The phase picks obtained with transfer-learned model are at least as accurate as the analyst’s, and lead to improved event locations. Moreover, the effort required for picking once the DNN is trained is a small fraction of the analyst’s.

Seismic sensors and seismic imaging have been widely used to monitor the geophysical properties of the subsurface. As subsurface engineering techniques advance, more precise monitoring systems are required. Seismic event catalogs and seismic velocity structures are two of the major outputs of seismic monitoring systems. Although seismic event catalogs and velocity structure are often studied separately, published reports suggest constraining them simultaneously can lead to better results. We conducted a double-difference seismic tomography analysis to constrain both the seismic event locations and the 3D seismic velocity structure. Passive seismic data collected from a geothermal research project in Lead, South Dakota were used to image a 3D volume on the scale of tens of meters. Specifically, around 18,500 P-wave and 8,900 S-wave arrival times from 1,874 seismic events were used. Checkerboard tests showed that the observed data can image the seismically active region well. We compared tomography results with fixed seismic event locations against those with updated event locations. Tomography results with updated event locations showed better fits to the observations and improved the seismic event catalog, showing sharper patterns compared to the original one. These patterns helped us monitor the seismically active fractures since the seismic events were mostly due to hydraulic stimulations. Two parallel fractures revealed by the updated seismic event catalog spatially correlated with independent borehole temperature observations. The average seismic velocity values of the well-constrained volume agreed to the first order with core sample measurements and active-source seismic surveys.