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  1. Abstract

    Continent‐scale observations of seismic phenomena have provided multi‐scale constraints of the Earth's interior. Of those analyzed, array‐based observations of slowness vector properties (backazimuth and horizontal slowness) and multipathing have yet to be made on a continental scale. Slowness vector measurements give inferences on mantle heterogeneity properties such as velocity perturbation and velocity gradient strength and quantify their effect on the wavefield. Multipathing is a consequence of waves interacting with strong velocity gradients resulting in two arrivals with different slowness vector properties and times. The mantle structure beneath the contiguous Unites States has been thoroughly analyzed by previous seismic studies and is data‐rich, making it an excellent testing ground to both analyze mantle structure with our approach and compare with other imaging techniques. We apply an automated array‐analysis technique to an SKS data set to create the first continent‐scale data set of multipathing and slowness vector measurements. We analyze the divergence of the slowness vector deviation field to highlight seismically slow and fast regions. Our results resolve several slow mantle anomalies beneath Yellowstone, the Appalachian mountains and fast anomalies throughout the mantle. Many of the anomalies cause multipathing in frequency bands 0.15–0.30 and 0.20–0.40 Hz which suggests velocity transitions over at most 500 km exist. Comparing our observations to synthetics created from tomography models, we find model NA13 (Bedle et al., 2021,https://doi.org/10.1029/2021GC009674) fits our data best but differences still remain. We therefore suggest slowness vector measurements should be used as an additional constraint in tomographic inversions and will lead to better resolved models of the mantle.

     
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  2. null (Ed.)
    SUMMARY Horizontal slowness vector measurements using array techniques have been used to analyse many Earth phenomena from lower mantle heterogeneity to meteorological event location. While providing observations essential for studying much of the Earth, slowness vector analysis is limited by the necessary and subjective visual inspection of observations. Furthermore, it is challenging to determine the uncertainties caused by limitations of array processing such as array geometry, local structure, noise and their effect on slowness vector measurements. To address these issues, we present a method to automatically identify seismic arrivals and measure their slowness vector properties with uncertainty bounds. We do this by bootstrap sampling waveforms, therefore also creating random sub arrays, then use linear beamforming to measure the coherent power at a range of slowness vectors. For each bootstrap sample, we take the top N peaks from each power distribution as the slowness vectors of possible arrivals. The slowness vectors of all bootstrap samples are gathered and the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to identify arrivals as clusters of slowness vectors. The mean of slowness vectors in each cluster gives the slowness vector measurement for that arrival and the distribution of slowness vectors in each cluster gives the uncertainty estimate. We tuned the parameters of DBSCAN using a data set of 2489 SKS and SKKS observations at a range of frequency bands from 0.1 to 1 Hz. We then present examples at higher frequencies (0.5–2.0 Hz) than the tuning data set, identifying PKP precursors, and lower frequency by identifying multipathing in surface waves (0.04–0.06 Hz). While we use a linear beamforming process, this method can be implemented with any beamforming process such as cross correlation beamforming or phase weighted stacking. This method allows for much larger data sets to be analysed without visual inspection of data. Phenomena such as multipathing, reflections or scattering can be identified automatically in body or surface waves and their properties analysed with uncertainties. 
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  3. null (Ed.)
    SUMMARY We determine crustal shear wave velocity structure and crustal thickness at recently deployed seismic stations across West Antarctica, using a joint inversion of receiver functions and fundamental mode Rayleigh wave phase velocity dispersion. The stations are from both the UK Antarctic Network (UKANET) and Polar Earth Observing Network/Antarctic Network (POLENET/ANET). The former include, for the first time, four stations along the spine of the Antarctic Peninsula, three in the Ellsworth Land and five stations in the vicinity of the Pine Island Rift. Within the West Antarctic Rift System (WARS) we model a crustal thickness range of 18–28 km, and show that the thinnest crust (∼18 km) is in the vicinity of the Byrd Subglacial Basin and Bentley Subglacial Trench. In these regions we also find the highest ratio of fast (Vs = 4.0–4.3 km s–1, likely mafic) lower crust to felsic/intermediate upper crust. The thickest mafic lower crust we model is in Ellsworth Land, a critical area for constraining the eastern limits of the WARS. Although we find thinner crust in this region (∼30 km) than in the neighbouring Antarctic Peninsula and Haag-Ellsworth Whitmore block (HEW), the Ellsworth Land crust has not undergone as much extension as the central WARS. This suggests that the WARS does not link with the Weddell Sea Rift System through Ellsworth Land, and instead has progressed during its formation towards the Bellingshausen and Amundsen Sea Embayments. We also find that the thin WARS crust extends towards the Pine Island Rift, suggesting that the boundary between the WARS and the Thurston Island block lies in this region, ∼200 km north of its previously accepted position. The thickest crust (38–40 km) we model in this study is in the Ellsworth Mountain section of the HEW block. We find thinner crust (30–33 km) in the Whitmore Mountains and Haag Nunatak sectors of the HEW, consistent with the composite nature of the block. In the Antarctic Peninsula we find a crustal thickness range of 30–38 km and a likely dominantly felsic/intermediate crustal composition. By forward modelling high frequency receiver functions we also assess if any thick, low velocity subglacial sediment accumulations are present, and find a 0.1–0.8-km-thick layer at 10 stations within the WARS, Thurston Island and Ellsworth Land. We suggest that these units of subglacial sediment could provide a source region for the soft basal till layers found beneath numerous outlet glaciers, and may act to accelerate ice flow. 
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