Over the past decades, seismic monitoring has been increasingly used to track glacial activities associated with ice loss. Many seismological studies focus on West Antarctica, whereas glacial seismicity in East Antarctica is much less studied. Here, we apply unsupervised deep learning to a dense nodal seismic array near Dålk Glacier, East Antarctica, operating from 6 December 2019 to 2 January 2020. An autoencoder is used to automatically extract event features, which are then input into a Gaussian mixture model for clustering. We divide the data into 50 clusters and merge them according to their temporal and spectral characteristics. The results reveal five main types of seismic signals: two groups with monochromatic and high frequencies, two groups with broadband frequency and short duration, and a group with mainly low frequency and long duration. By comparing the environmental conditions (wind, temperature and tides), we infer that the two monochromatic groups are wind-induced vibrations of the near-station flag markers and topography; the two broadband groups are likely thermal contractions on the blue ice surface and stick-slip events at the ice base; and the low-frequency events are water-filled basal crevassing and iceberg calving. In particular, we observe one type of low-frequency event preceded by high-frequency onset, which is likely basal crevassing near the grounding line of Dålk Glacier and predominantly occurred during rising tides. Our findings show that deep clustering is effective in identifying a wide range of glacial seismic events and can contribute to the rapid growth of passive glacier seismic monitoring.

中文翻译：

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深层聚类揭示了东南极洲达尔克冰川附近环境调节的冰川地震活动

在过去的几十年里，地震监测越来越多地用于跟踪与冰损失相关的冰川活动。许多地震学研究都集中在南极洲西部，而南极洲东部冰川地震活动的研究则少得多。在这里，我们将无监督深度学习应用于南极洲东部 Dålk 冰川附近的密集节点地震阵列，该阵列于 2019 年 12 月 6 日至 2020 年 1 月 2 日运行。自动编码器用于自动提取事件特征，然后将其输入到高斯混合模型中聚类。我们将数据分为 50 个簇，并根据它们的时间和光谱特征将它们合并。结果揭示了五种主要类型的地震信号：两组具有单色和高频，两组具有宽带频率和短持续时间，以及一组主要是低频和长持续时间。通过比较环境条件（风、温度和潮汐），我们推断这两个单色组是近站旗标和地形的风致振动；这两个宽带组可能是蓝色冰表面的热收缩和冰基的粘滑事件；低频事件是充满水的基底裂缝和冰山崩解。特别是，我们观察到一类低频事件先于高频事件，这可能是多尔克冰川接地线附近的基底裂缝，主要发生在涨潮期间。我们的研究结果表明，深层聚类可以有效识别各种冰川地震事件，并有助于被动冰川地震监测的快速增长。