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.

冰下地震活动提供了在震中位置和时间监测难以到达的冰床的机会。冰川下面可以是岩石或耕耘物，这是对河床力学的一阶控制。每种床类型都显示出速度减弱，这是不稳定滑移所必需的，但速度减弱要强得多，并且对于耕床床来说，速度减弱会在更长的距离上演进一个数量级以上。利用去硬化双直剪装置，我们发现了两种床类型在冷冻温度和高滑移率下不稳定的条件。在粘滑应力下降过程中，我们用冻结在冰中的压电传感器记录了声发射。事件波形的两个群体在视觉上看起来相似并且在统计特征上重叠。我们实施了一套监督机器学习算法来对记录的波形和频谱的床类型进行分类，预测准确度在 65-80% 之间。随机森林分类器是可解释的，显示了初始振荡峰值和更高频率能量的重要性。与岩石发射相比，耕床通常具有更高的摩擦力和由此产生的应力下降，具有更多的脉冲先到和更多的高频内容，但岩床可以产生许多类似耕床的事件。地震特征可以增强对冰下地震活动的地床条件和力学的解释。