The determination of seismic event locations with sparse networks or single-borehole
systems remains a significant challenge in observational seismology. Leveraging the …

Bayesian methods provide a valuable framework for rigorously quantifying the model
uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion …

Bayesian inference has become an important tool to solve inverse problems and to quantify
uncertainties in their solutions. Variational inference is a method that provides probabilistic …

Full waveform inversion (FWI) creates high resolution models of the Earth's subsurface
structures from seismic waveform data. Due to the non-linearity and non-uniqueness of FWI …

This thesis presents a collection of works centered on computational statistics in Bayesian
seismology. Bayesian seismology interprets inverse problems in seismology as questions of …

This work considers regularization of seismic full waveform inversion (FWI), where the
sparsity of the underlying model is exploited together with spatial correlations using the so …