Seasonal and spatial variations in the ocean-coupled ambient wavefield of the Ross Ice Shelf

Baker, Michael G.; Aster, R. C.; Anthony, Robert E.; Chaput, Julien; Wiens, Douglas A.; Nyblade, A.; Bromirski, Peter D.; Gerstoft, Peter; Stephen, Ralph A.

doi:10.1017/jog.2019.64

Cited by 15 publications

(31 citation statements)

References 63 publications

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“…This variation is bimodal between the austral summer and winter and reflects the attenuation of wind-sea and ocean gravity waves in the Ross Sea by the formation of spatially continuous sea ice during the winter months. The absence of extensive, continuous sea ice determines the onset and termination dates of the ‘summer’ high-noise state analyzed in Baker and others (2019). We similarly subdivide our catalog into ‘summer’ earthquakes occurring during the approximate open-water interval between 1 December and 31 March, and ‘winter’ earthquakes occurring during the remainder of the year during which sea ice is broadly contiguous at the ice shelf front.…”

Section: Methodsmentioning

confidence: 95%

“…The ambient noise field of the RIS in the 0.4–30 s period band has seasonal variations of ~5–20 dB (Baker and others, 2019). This variation is bimodal between the austral summer and winter and reflects the attenuation of wind-sea and ocean gravity waves in the Ross Sea by the formation of spatially continuous sea ice during the winter months.…”

Section: Methodsmentioning

confidence: 99%

“…1) was installed across the RIS for a 2-year study of oceanic, cryospheric and solid Earth processes (Bromirski and others, 2015) by recording elastic and flexural-gravity waves on the ice shelf (Bromirski and Stephen, 2012). Ocean signals recorded atop large tabular floating ice bodies include tsunamis and infragravity waves (Bromirski and others, 2017), ocean swell (Okal and MacAyeal, 2006; Cathles and others, 2009; Baker and others, 2019), waves from remote-calving events (MacAyeal and others, 2009) and ice–ice and ice–seafloor interactions and their seismic and acoustic radiation (Talandier and others, 2006; Dowdeswell and Bamber, 2007; MacAyeal and others, 2008; Martin and others, 2010). The RIS deployment additionally expanded opportunities for solid Earth studies in the sparsely-sampled Ross Embayment sector of the Antarctic plate; Shen and others (2018) applied ambient noise Rayleigh wave tomography to invert mantle S-wave velocity structure, and White-Gaynor and others (2019) applied teleseismic P-wave arrival time tomography to invert mantle P-wave velocity structure.…”

Section: Introductionmentioning

confidence: 99%

“…The ambient seismic environment of the RIS has been previously documented at short periods (<1 s) by Diez and others (2016) and Chaput and others (2018), and for longer periods (>30 s) by MacAyeal and others (2006), Cathles and others (2009), Bromirski and others (2017) and Chen and others (2018). For intermediate periods (0.5–20 s), Baker and others (2019) present seasonal and geographic variations in the RIS noise field and discuss potential source mechanisms. Our current signal-noise analysis makes extensive use of Baker and others (2019) and may be viewed as a companion piece to that work.…”

Section: Introductionmentioning

confidence: 99%

“…For intermediate periods (0.5–20 s), Baker and others (2019) present seasonal and geographic variations in the RIS noise field and discuss potential source mechanisms. Our current signal-noise analysis makes extensive use of Baker and others (2019) and may be viewed as a companion piece to that work.…”

Section: Introductionmentioning

confidence: 99%

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Teleseismic earthquake wavefields observed on the Ross Ice Shelf

et al. 2020

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Observations of teleseismic earthquakes using broadband seismometers on the Ross Ice Shelf (RIS) must contend with environmental and structural processes that do not exist for land-sited seismometers. Important considerations are: (1) a broadband, multi-mode ambient wavefield excited by ocean gravity wave interactions with the ice shelf; (2) body wave reverberations produced by seismic impedance contrasts at the ice/water and water/seafloor interfaces and (3) decoupling of the solid Earth horizontal wavefield by the sub-shelf water column. We analyze seasonal and geographic variations in signal-to-noise ratios for teleseismic P-wave (0.5–2.0 s), S-wave (10–15 s) and surface wave (13–25 s) arrivals relative to the RIS noise field. We use ice and water layer reverberations generated by teleseismic P-waves to accurately estimate the sub-station thicknesses of these layers. We present observations consistent with the theoretically predicted transition of the water column from compressible to incompressible mechanics, relevant for vertically incident solid Earth waves with periods longer than 3 s. Finally, we observe symmetric-mode Lamb waves generated by teleseismic S-waves incident on the grounding zones. Despite their complexity, we conclude that teleseismic coda can be utilized for passive imaging of sub-shelf Earth structure, although longer deployments relative to conventional land-sited seismometers will be necessary to acquire adequate data.

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Section: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Teleseismic earthquake wavefields observed on the Ross Ice Shelf

et al. 2020

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Constraining Floating Ice Shelf Structures by Spectral Response of Teleseismic P‐Wave Coda: Ross Ice Shelf, Antarctica

Phạm

Tkalčić

2021

JGR Solid Earth

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The recent deployment of a broadband seismic array on the floating ice shelf in the Antarctica's Ross sea presents a great opportunity to study the shelf structure using broadband seismic data. In this study, we develop a further improvement of the P‐wave coda autocorrelation method, which proved capable of characterizing grounded ice‐cap structures. Ice shelves are floating ice sheets connected to a landmass, and in order to decipher their structures, a water layer has to be added to the problem. We construct the power spectrum stacks of P‐wave coda data, waveform records that immediately follow P arrivals, in the spectral domain, which are equivalent, via a Fourier transform, to widely used autocorrelograms in the time domain. At half of temporary seismic stations under consideration, we report prominent resonant peaks in the spectral autocorrelograms, associated with the ice‐water configuration of the ice shelf. The lack of clear resonant pattern for the rest of the stations is suspected due to a high noise level in the icy environment and significant lateral heterogeneity at the local scale. Subsequently, we develop a formalism to explain the observed resonance and devise a grid‐search scheme to estimate ice‐ and water‐thicknesses underneath the stations. Our water‐thickness estimates agree well with the previously documented measurements, but there is a discrepancy in the ice thickness results. Therefore, the method has a great potential to complement the existing ice‐shelf model, to be used in future monitoring applications of ice shelves, or near‐future space exploration to icy planets.

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Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

et al. 2021

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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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Seasonal and spatial variations in the ocean-coupled ambient wavefield of the Ross Ice Shelf

Cited by 15 publications

References 63 publications

Teleseismic earthquake wavefields observed on the Ross Ice Shelf

Teleseismic earthquake wavefields observed on the Ross Ice Shelf

Constraining Floating Ice Shelf Structures by Spectral Response of Teleseismic P‐Wave Coda: Ross Ice Shelf, Antarctica

Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

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