Picking Induced Seismicity with Deep Learning (piSDL)

Training deep-learning picking models with several published data sets can be easily done through the Python toolbox SeisBench. Most of the data sets contain earthquakes recorded at local, regional and teleseismic distances, with only limited data in the low magnitude, close distance region. Applying current published PhaseNet models to induced seismicity data leads to only a few events being detected and trained PhaseNet models are not able to outperform well-established workflows in seismology. Here we present a new seismological data set and trained PhaseNet models for picking induced seismicity with deep-learning (piSDL). PhaseNet was trained with 171,182 three component waveforms from 40,576 events. Noise samples were added in the training data set to reduce the number of false picks. In this study, we noticed that a good earthquake training data set and noise samples from the analysed area are both important to detect more seismic events with a newly trained PhaseNet model. We validated our new PhaseNet models at a geothermal site in Rittershoffen (France). The models trained with the new data set and noise samples clearly outperform PhaseNet’s original published model and traditional methods in seismology by detecting up to 62% more events compared to a seismicity catalogue published by an agency.