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Access Type
WSU Access
Date of Award
January 2025
Degree Type
Thesis
Degree Name
M.S.
Department
Geology
First Advisor
Scott Burdick
Abstract
We introduce a probabilistic three-dimensional tomography approach to image Alaska’s lithosphere using ambient-noise Rayleigh wave dispersion and Poisson Voronoi (PV) inversion. Empirical Green’s functions from ∼1,000 broadband stations are processed via an automated Frequency–Time Analysis workflow to extract phase velocities between 8 and 50 s periods, employing uniform spectral whitening and a strict signal-to-noise ratio threshold. These measurements are converted into depth-dependent sensitivity kernels using direct inversion theory and mapped onto a 3D grid. A probabilistic PV method with nonlinear radial flattening (γ = 1.2) performs 500 low-dimensional inversions of 3,000 randomly distributed Voronoi cells, yielding an ensemble mean shear-wave velocity model to 240 km depth and corresponding uncertainty estimates. The resulting velocity distribution delineates major tectonic terranes and highlights areas of elevated uncertainty associated with sparse data coverage. This framework provides a robust foundation for joint inversion with body wave data and enhanced crustal corrections in seismic studies.
Recommended Citation
Allen, Paul Tyler, "Poisson Voronoi Tomography Of Alaska With 3d Rayleigh Wave Dispersion Kernels" (2025). Wayne State University Theses. 1001.
https://digitalcommons.wayne.edu/oa_theses/1001