  Automatic detection of low-magnitude earthquakes has become an increasingly
important research topic in recent years due to a sharp increase in induced
seismicity around the globe. The detection of low-magnitude seismic events is
essential for microseismic monitoring of hydraulic fracturing, carbon capture
and storage, and geothermal operations for hazard detection and mitigation.
Moreover, the detection of micro-earthquakes is crucial to understand the
underlying mechanisms of larger earthquakes. Various algorithms, including deep
learning methods, have been proposed over the years to detect such
low-magnitude events. However, there is still a need for improving the
robustness of these methods in discriminating between local sources of noise
and weak seismic events. In this study, we propose a convolutional neural
network (CNN) to detect seismic events from shallow borehole stations in
Groningen, the Netherlands. We train a CNN model to detect low-magnitude
earthquakes, harnessing the multi-level sensor configuration of the G-network
in Groningen. Each G-network station contains four geophones at depths of 50,
100, 150, and 200 meters. Unlike prior deep learning approaches that use
3-component seismic records only at a single sensor level, we use records from
the entire borehole as one training example. This allows us to train the CNN
model using moveout patterns of the energy traveling across the borehole
sensors to discriminate between events originating in the subsurface and local
noise arriving from the surface. We compare the prediction accuracy of our
trained CNN model to that of the STA/LTA and template matching algorithms on a
two-month continuous record. We demonstrate that the CNN model shows
significantly better performance than STA/LTA and template matching in
detecting new events missing from the catalog and minimizing false detections.
