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Abstract Using 155 distributed seismic stations spanning Alaska and western Canada, we document how environmental factors like storms and sea ice influence microseismic noise. We examine power spectral densities of continuous seismic data and focus on secondary microseisms (5–10 s) and short period secondary microseisms (1–2 s) from 2018 to 2021. We cross‐correlate the height of ocean waves across the region with the power spectral density time series. We find that the Gulf of Alaska is the dominant source of secondary microseisms in Alaska. The eastern Gulf, in particular, produces more energetic secondary microseisms despite, at times, lower overall wave amplitudes. We find that the short period secondary microseismic noise is produced in the coastal waters and attenuates quickly moving inland. We show that this band is heavily modulated by the influence of sea ice in the coastal ocean by comparing it with sea ice concentrations. We also document how these two microseismic bands vary seasonally and spatially as they respond to different environmental phenomena. We find that this seismic energy closely tracks the seasonal arrival and departure of sea ice in the coastal waters. We also compare the inter‐annual variability of short period secondary microseisms in the northern Arctic from 2009 to 2023 with shorefast ice data. The findings of this study are crucial for monitoring global climate change through seismology. 

SUMMARY This study examines the feature space of seismic waveforms often used in machine learning applications for seismic event detection and classification problems. Our investigation centres on the southern Alaska region, where the seismic record captures diverse seismic activity, notably from the calving of marine-terminating glaciers and tectonic earthquakes along active plate boundaries. While the automated discrimination of earthquakes and glacier quakes is our nominal goal, this data set provides an outstanding opportunity to explore the general feature space of regional seismic phases. That objective has applicability beyond ice quakes and our geographic region of study. We make a noteworthy discovery that features rooted in the spectral content of seismic waveforms consistently outperform statistical and temporal features. Spectral features demonstrate robust performance, exhibiting resilience to class imbalance while being minimally impacted by factors such as epicentral distance and signal-to-noise ratio. We also conduct experiments on the transferability of the model and find that transferability primarily depends on the appearance of the waveforms. Finally, we analyse misclassified events and find examples that are identified incorrectly in the original regional catalogue.