Subglacial seismicity presents the opportunity to monitor inaccessible
glacial beds at the epicentral location and time. Glaciers can be
underlain by rock or till, a first order control on bed mechanics.
Velocity-weakening, necessary for unstable slip, has been shown for each
bed type, but is much stronger and evolves over more than an order of
magnitude longer distances for till beds. Utilizing a de-stiffened
double direct shear apparatus, we found conditions for instability at
freezing temperatures and high slip rates for both bed types. During
stick-slip stress-drops, we recorded acoustic emissions with
piezoelectric transducers frozen into the ice. The two populations of
event waveforms appear visually similar and overlap in their statistical
features. We implemented a suite of supervised machine learning
algorithms to classify the bed type of recorded waveforms and spectra,
with prediction accuracy between 65% - 80%. The Random Forest
Classifier is interpretable, showing the importance of initial
oscillation peaks and higher frequency energy. Till beds have generally
higher friction and resulting stress-drops, with more impulsive first
arrivals and more high frequency content compared to rock emissions, but
rock beds can produce many till-like events. Seismic signatures could
enhance interpretation of bed conditions and mechanics from subglacial
seismicity.