This thesis is a two-way transfer of knowledge between cosmology and seismology, aiming to substantially advance imaging methods and uncertainty quantification in both fields.
I develop a method using wavelets to simulate the uncertainty in a set of existing global seismic tomography images to assess the robustness of mantle plume-like structures. Several plumes are identified, including one that is rarely discussed in the seismological literature. I present a new classification of the most likely deep mantle plumes from my automated method, potentially resolving past discrepancies between deep mantle plumes inferred by visual analysis of tomography models and other geophysical data.
Following on from this, I create new images of the upper-most mantle and their associated uncertainties using a sparsity-promoting wavelet prior and an advanced probabilistic inversion scheme. These new images exhibit the expected tectonic features such as plate boundaries and continental cratons. Importantly, the uncertainties obtained are physically reasonable and informative, in that they reflect the heterogenous data distribution and also highlight artefacts due to an incomplete forward model. These inversions are a first step towards building a fully probabilistic upper-mantle model in a sparse wavelet basis.
I then apply the same advanced probabilistic method to the problem of full-sky cosmological mass-mapping.
However, this is severely limited by the computational complexity of high-resolution spherical harmonic transforms. In response to this, I use, for the first time in cosmology, a trans-dimensional algorithm to build galaxy cluster-scale mass-maps. This new approach performs better than the standard mass-mapping method, with the added benefit that uncertainties are naturally recovered.
With more accurate mass-maps and uncertainties, this method will be a valuable tool for cosmological inference with the new high-resolution data expected from upcoming galaxy surveys, potentially providing new insights into the interactions of dark matter particles in colliding galaxy cluster systems
