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.