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Abstract The Ross Ice Shelf (RIS) is host to a broadband, multimode seismic wavefield that is excited in response to atmospheric, oceanic and solid Earth source processes. A 34-station broadband seismographic network installed on the RIS from late 2014 through early 2017 produced continuous vibrational observations of Earth's largest ice shelf at both floating and grounded locations. We characterize temporal and spatial variations in broadband ambient wavefield power, with a focus on period bands associated with primary (10–20 s) and secondary (5–10 s) microseism signals, and an oceanic source process near the ice front (0.4–4.0 s). Horizontal component signals on floating stations overwhelmingly reflect oceanic excitations year-round due to near-complete isolation from solid Earth shear waves. The spectrum at all periods is shown to be strongly modulated by the concentration of sea ice near the ice shelf front. Contiguous and extensive sea ice damps ocean wave coupling sufficiently so that wintertime background levels can approach or surpass those of land-sited stations in Antarctica.
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Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica
Abstract Advances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34‐station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for the comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower‐dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island.
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- PAR ID:
- 10366949
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 126
- Issue:
- 9
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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