Azimuthal anisotropy in Bayesian surface wave tomography: application to northern Cascadia and Haida Gwaii, British Columbia

Abstract

Summary Surface wave tomography is a valuable tool for constraining azimuthal anisotropy at regional scales. However, sparse and uneven coverage of dispersion measurements make meaningful uncertainty estimation challenging, especially when applying subjective model regularization. This paper considers azimuthal anisotropy constrained by measurements of surface wave dispersion data within a Bayesian trans-dimensional (trans-d) tomographic inversion. A recently-proposed alternative model parameterization for trans-d inversion is implemented in order to produce more realistic models than previous studies considering trans-d surface wave tomography. The reversible-jump Markov-chain Monte Carlo sampling technique is used to numerically estimate the posterior probability density of the model parameters. Isotropic and azimuthally-anisotropic components of surface wave group velocity maps (and their associated uncertainties) are estimated while avoiding model regularization and allowing model complexity to be determined by the data information content. Furthermore, data errors are treated as unknown, and solved for within the inversion. The inversion method is applied to measurements of surface wave dispersion from regional earthquakes recorded over northern Cascadia and Haida Gwaii, a region of complex active tectonics but highly heterogeneous station coverage. Results for isotropic group velocity are consistent with previous studies that considered the southern part of the study region over Cascadia. Azimuthal anisotropic fast-axis directions are generally margin-parallel between Vancouver Island and Haida Gwaii, with a small change in direction and magnitude along the margin which may be attributed to the changing tectonic regime (from subduction to transform tectonics). Estimated errors on the dispersion data (solved for within the inversion) reveal a correlation between surface wave period and the dependence of data errors on travel path length. This paper demonstrates the value of considering azimuthal anisotropy within Bayesian tomographic inversions. Furthermore, this work provides structural context for future studies of tectonic structure and dynamics of northern Cascadia and Haida Gwaii, with the aim of improving our understanding of seismic and tsunami hazards.

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

“…Recently, Gosselin et al. (2021) showed that, at periods ranging from 10 to 50 s over similar propagation distances considered in our work, updating the ray paths did not significantly affect the seismic velocity model.…”

“…We also note that the magnitude of azimuthal anisotropy is slightly overestimated, as pointed out in Gosselin et al. (2021), likely due to the discrepancy between the input model (which is a discrete checkerboard) and the underlying model parameterization in the inversion, which is inherently smooth. We estimate that changes in azimuthal anisotropy on a spatial scale of ∼450 km can be well resolved across the NCC.…”

“…It is worth noting that previous studies on transdimensional Bayesian surface-wave tomography implementing ray tracing considered much shorter period dispersion data as well as much longer propagation paths (e.g., Bodin & Sambridge, 2009;Bodin et al, 2012). Recently, Gosselin et al (2021) showed that, at periods ranging from 10 to 50 s over similar propagation distances considered in our work, updating the ray paths did not significantly affect the seismic velocity model.…”

“…These tests reveal the isotropic and azimuthally anisotropic structure expected to be resolved for structure of varying spatial extent, for the data at a period of 20 s. Tests at other periods are expected to demonstrate similar patterns, with minor variations reflected minor variability in interfrequency path coverage. As discussed in Bodin et al (2012) and Gosselin et al (2021), Bayesian tomography is highly sensitive to data errors which are treated as unknown in the inversion procedure. Without accurate knowledge of true data errors, these synthetic tests provide only a limited view of the data information content, under idealized conditions.…”

“…(2012) and Gosselin et al. (2021), Bayesian tomography is highly sensitive to data errors which are treated as unknown in the inversion procedure. Without accurate knowledge of true data errors, these synthetic tests provide only a limited view of the data information content, under idealized conditions.…”