loading page

Characterization of Seismicity from Different Glacial Bed Types: Machine Learning Classification of Laboratory Stick-Slip Acoustic Emissions
  • +1
  • Seth Saltiel,
  • Nathan Groebner,
  • Theresa Sawi,
  • Christine McCarthy
Seth Saltiel
Cornell University

Corresponding Author:[email protected]

Author Profile
Nathan Groebner
University of Minnesota
Author Profile
Theresa Sawi
Lamont-Doherty Earth Observatory
Author Profile
Christine McCarthy
Lamont-Doherty Earth Observatory, Columbia University
Author Profile


Subglacial seismicity provides the opportunity to monitor inaccessible glacial beds at the epicentral location and time. Glaciers can be underlain by rock or till, which determines the mechanics of slip and, if unstable, characteristics of resulting seismicity. Utilizing a double direct shear apparatus, we found conditions for instability at freezing temperatures and high slip rates for both bed types, although with very different frictional evolution. During stick-slip stress-drops, we recorded acoustic emissions with piezoelectric transducers frozen into the ice. Supervised machine learning can classify recorded waveforms and spectra as coming from rock or till beds. The Random Forest Classifier is interpretable, with the prediction based on the initial oscillation peaks and high frequency energy. Till events are generally higher stress-drop, with more impulsive first arrivals compared to rock waveforms. These seismic signatures of mechanical slip processes and associated bed conditions can potentially greatly enhance interpretation of subglacial seismic data.
22 Feb 2023Submitted to ESS Open Archive
27 Feb 2023Published in ESS Open Archive