Global Upper Mantle Azimuthal Anisotropy From Probabilistic Tomography
Abstract:
In this work, we apply a model space approach, the Neighborhood Algorithm (NA) of Sambridge (1999), to determine quantitative model uncertainties and parameter trade-offs. First, the NA generates an ensemble of models with a sampling density that increases toward the best fitting regions of the model space, and then performs a Bayesian appraisal of the models obtained that allows us to determine the likelihood of azimuthal anisotropy in different region of Earth’s interior. Such approaches have the advantage of sampling the model null-space, and therefore provide more reliable model uncertainties than traditional inverse techniques. We use YBaniSV13 as initial model, and search the model space around it, allowing for large enough deviations to test the robustness of the anisotropy amplitude. We compare results from a model space search based on the chi-square misfit and from a model space search based on the variance reduction, which is another useful measure of data fit that is independent of data uncertainties. Preliminary results for the chi-square driven search show that most likely model that is close to YBaniSV13, but uncertainties are rather large even in the shallow mantle where azimuthal anisotropy is usually considered to be well-resolved.
