Surface‐Wave Tomography of the Northern Canadian Cordillera Using Earthquake Rayleigh Wave Group Velocities

Estève, Clément; Gosselin, J.; Audet, Pascal; Schaeffer, A. J.; Schutt, D.; Aster, R. C.

doi:10.1029/2021jb021960

Cited by 25 publications

(52 citation statements)

References 90 publications

(187 reference statements)

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“…(2020) and the upper region from the surface wave tomography models of Estève et al. (2021). It appears that some overlap exists between the regions of low seismic velocity and our modeled vertical mantle velocities under the Mackenzie Mountains.…”

Section: Discussionmentioning

confidence: 99%

Investigating the Effect of Mantle Flow and Viscosity Structure on Surface Velocities in Alaska Using 3‐D Geodynamic Models

2022

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We utilize 3‐D finite element geodynamic models, incorporating long‐term kinematic estimates of upper plate motion, to better understand the roles that viscosity structure and mantle tractions play in generating plate motions and continental interior deformation in Alaska. Surface deformation in the Pacific‐North American plate boundary zone in Alaska and northwest Canada is strongly influenced by the complex interactions between flat‐slab subduction, gravitational collapse and mantle tractions. Predictions of long‐term tectonic block motion derived from recent Global Positioning System datasets (GPS) reveal surface motions atypical of other continental convergent plate boundary zones. Specifically, in northern and northwestern Alaska, southeastward motion is observed directed back toward the plate boundary. Geodynamic models that incorporate a southeastward directed long‐wavelength mantle traction of ∼2.5–3.8 MPa best replicate surface velocities in Alaska. These mantle tractions, in conjunction with the collision of the Yakutat microplate, appear to drive the uplift and deformation in the Mackenzie Mountains. Furthermore, the extent of the northward motion in southern and central Alaska is controlled by the location of the leading edge of the Yakutat flat slab.

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Section: Discussionmentioning

confidence: 99%

Investigating the Effect of Mantle Flow and Viscosity Structure on Surface Velocities in Alaska Using 3‐D Geodynamic Models

2022

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“…(Middle and bottom rows) Transects A‐A’, B‐B’ and CC’ through the P‐wave, S‐wave and V P / V S models. Black dashed line shows Moho depth estimates along transect from (Estève et al., 2021). Relocated seismicity within 3 km from depth 20 km are plotted in the top row; within 40 km from each transect are plotted in the middle and bottom rows.…”

Section: Resultsmentioning

confidence: 99%

“…The hatched area shows inferred crustal thickening at the Beaufort Sea continental margin. Black dashed line shows Moho depth estimates along transect from (Estève et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

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Seismic Evidence for a Weakened Thick Crust at the Beaufort Sea Continental Margin

Estève

Liu

Koulakov

et al. 2022

Geophysical Research Letters

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The Canadian Beaufort Sea continental margin of northwestern Canada is a Cenozoic convergent margin, potentially representing a rare case of incipient subduction. Here, we produce P‐ and S‐wave seismic velocity models of the crust and the uppermost mantle using recordings from regional earthquakes. Our models reveal a northwest‐dipping very low‐velocity anomaly within the crust (δV up to −15%) beneath the Romanzof Uplift. We interpret this low‐velocity feature to correspond to a weaker and thicker crust due to shortening and stacking of igneous and sedimentary rocks. The co‐location of the thickened crust and lack of present‐day seismicity indicates that north‐south compression is accommodated by slow, aseismic deformation in the narrow margin beneath the Romanzof Uplift or more broadly offshore. Neither interpretation requires nor precludes a subduction initiation process.

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“…Dispersion inversion using energy likelihood 11Minsley 2011;Ray & Key 2012;Young et al 2013;Piana Agostinetti et al 2015;Saygin et al 2015;Galetti et al 2017;Burdick & Lekić 2017;Biswas & Sen 2017;Zhu & Gibson 2018;Xiang et al 2018; Zhang et al 2020a,b;Estève et al 2021;Mousavi et al 2021;Hallo et al 2021). In this study we use the method to solve the surface wave dispersion inversion problem.In rj-McMC one constructs a (Markov) chain of samples by perturbing the current model m using a proposal distribution q(m |m) to generate a new model m , and by accepting or rejecting this new model with a probability α(m |m) called the acceptance ratio:…”

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confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang

Zheng

Curtis

2022

Geophysical Journal International

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Summary Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis of data recorded on a local array of receivers. The apparent phase velocity at each frequency of the surface waves travelling across the array is that at which the f-c spectrum has maximum amplitude. However, because of potential contamination by other wave arrivals or due to complexities in the velocity structure the f-c spectrum often has multiple maxima at each frequency for each mode. These introduce errors and ambiguity in the picked phase velocities, and consequently the estimated shear velocity structure can be biased, or may not account for the full uncertainty in the data. To overcome this issue we introduce a new method which directly uses the spectrum as the data to be inverted. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood, and efficiently since it removes the need to pick phase velocities. We therefore conclude that the energy likelihood function can be a valuable tool for surface wave dispersion inversion in practice.

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Surface‐Wave Tomography of the Northern Canadian Cordillera Using Earthquake Rayleigh Wave Group Velocities

Cited by 25 publications

References 90 publications

Investigating the Effect of Mantle Flow and Viscosity Structure on Surface Velocities in Alaska Using 3‐D Geodynamic Models

Investigating the Effect of Mantle Flow and Viscosity Structure on Surface Velocities in Alaska Using 3‐D Geodynamic Models

Seismic Evidence for a Weakened Thick Crust at the Beaufort Sea Continental Margin

Surface wave dispersion inversion using an energy likelihood function

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