The oceanic lithosphere makes up approximately two-thirds of the surface of the earth. Oceanic crust, which is underlain by lithospheric mantle, is formed at mid-ocean ridges and is shaped by a combination of igneous accretionary processes at and near the ridge axis, and post-emplacement tectonic and hydrothermal processes as it evolves. Through time the crust is covered by sediments, sealing it from the overlying ocean, which influences hydrothermal circulation and cooling in the lithosphere below. Finally, oceanic lithosphere is subsumed at subduction zones. In this thesis I utilize seismic data to investigate the oceanic lithosphere from formation to near subduction using seismic datasets from the East Pacific Rise (EPR) and the Juan de Fuca (JdF) plate. In my first chapter I investigate the hypothesis that eustatic sea level fluctuations induced by the glacial cycles of the Pleistocene influence mantle-melting at mid-ocean ridges (MORs) using a unique bathymetry and crustal thickness dataset derived from a 3D multi-channel seismic (MCS) investigation of the East Pacific Rise from 9°42’ to 57’N. The results of this study show variations in crustal thickness and bathymetry at timescales associated with Pleistocene glacial cycles, supporting the inference that mantle melt supply to MOR may be modulated by sea level variations. Further investigations of the hypothesis that sea level variations may influence MOR dynamics are presented in appendices one and two. In appendix one I explore whether variations at the timescales of glacial cycles are apparent in MCS datasets from the intermediate spreading JdF ridge as well as bathymetry data from the fast spreading EPR. In appendix two I present a case study in which I re-examine the crustal thickness and bathymetry data from the northern EPR presented in chapter one in order to assess how fine-scale segmentation of the ridge axis appears in data, and compare different methodological approaches to describing MOR generated topography. In my second chapter I present results from a wide-angle controlled source seismic experiment conducted along a transect crossing the JdF plate from ~20 km east of the axis at the Endeavour segment of the JdF ridge to the Cascadia margin off of Washington state. I utilize a joint refraction-reflection traveltime inversion to generate a two-dimensional tomographic Vp model of the sediments, crust and upper mantle. Analysis of this Vp model, along with characterization of the basement topography along the transect, reveals three intervals (spanning millions of years) of distinct crust and upper mantle properties indicating a spatially heterogeneous JdF plate which is interpreted as inherited from changes in the mode of accretion at the paleo-JdF ridge, differences in plate interior processes, and deformation near the subduction zone. In my third chapter I present results of a MCS study of the sediment section conducted along a transect spanning ~350 km along the Cascadia margin from offshore southern Oregon to offshore Washington state. In this study I utilize prestack depth migrated MCS data to describe the reflectivity of the sediment section and invert for impedance and density. I also present results of amplitude variation with angle of incidence analysis conducted using pre-stack seismic gathers. Results indicate along margin variations in the characteristics of the sediments as well as complex changes in the stress state along the Cascadia margin. Synthesis of these analyses provides an in-depth assessment of patterns of sedimentation and properties of the sediment section as it experiences the effects of the onset of subduction.

Recent advances in computational power, as well as the hard work of a handful of brilliant scientists, have made Bayesian inversion of geophysical observations possible. This development is highly significant, as it permits the quantification of uncertainty, not only on the inverted model parameters, but also on related properties of interest. This dissertation focuses on the application of a particular kind of Bayesian inversion – trans-dimensional Markov chain Monte Carlo – to electromagnetic data, specifically airborne transient electromagnetic, magnetotelluric, and surface-towed controlled source electromagnetic data. In chapters 2-4, these data, both real and synthetic, are inverted for 1D models of subsurface electrical resistivity. In chapter 5, magnetotelluric data are inverted for 2D models of resistivity – the first time, to the best of my knowledge, that trans-dimensional Bayesian inversion of magnetotelluric data for 2D models has been achieved. In each instance, the uncertainty on bulk resistivity provided by the Bayesian inversion is used to estimate uncertainty on related subsurface properties, including pore fluid resistivity and salinity, porosity, melt fraction, melt volatile content, and bulk mantle volatile inventory. Chapter 1 introduces the topic of Bayesian inversion of electromagnetic data. Chapter 2 concerns trans-dimensional Bayesian inversion of airborne transient electromagnetic data. These data were collected above Taylor Glacier in the McMurdo Dry Valleys region of Antarctica in 2011, and were inverted using deterministic inverse methods to image a conductive channel beneath the glacier, interpreted as a package of brine-saturated sediments. The Bayesian inversion of these data confirms the existence of a conductive channel and provides quantitative uncertainties on the resistivity as a function of depth. These uncertainties are used in conjunction with Archie’s Law to estimate uncertainty on the resistivity of the pore fluids in the sediments. Additionally, the Kullback-Leibler divergence – a statistical measure of the dissimilarity of two distributions – is introduced as a measure of how much influence the observations have on the model parameters as a function of depth. The utility of Bayesian inversion in estimating the noise floor necessary to effectively resolve model structure is demonstrated. In chapter 3, a joint Bayesian framework for inverting electromagnetic data is introduced. A modified version of the algorithm utilized in chapter 2 is applied to jointly invert marine magnetotelluric and surface-towed controlled source electromagnetic data. These data were collected offshore New Jersey in 2015 to image a freshwater aquifer in the continental shelf. Deterministic inversions of this data clearly image a resistive body at depths consistent with low salinity from bore hole measurements collocated with the electromagnetic survey. The Bayesian inversion of this data set again confirms the existence of the resistive region while further providing uncertainty on the inverted resistivity with depth. In some instances, bimodality in the posterior distribution is found, demonstrating the importance of Bayesian inverse methods for fully exploring the model space. The uncertainty on bulk resistivity is used in conjunction with Archie’s Law and the porosity from bore hole measurements in a Monte Carlo framework to estimate uncertainty in the salinity of the pore water as a function of depth for three well locations. These estimates match well with measured salinities at these locations, validating the use of the Bayesian posterior in the context of a Monte Carlo framework to estimate uncertainty on related physical properties. In chapter 4, seafloor magnetotelluric data are again inverted for 1D models of subsurface resistivity, this time to image a conductive channel at the base of the lithosphere. The data are a subset of a deployment of 50 Broadband MT instruments on the seafloor above the Cocos plate offshore Nicaragua. Deterministic inversions of this data revealed a conductive structure at 45-70 km depth, beneath the Cocos plate. This earlier analysis concluded that melt was required at the lithosphere-asthenosphere boundary (LAB) to explain the inverted resistivity, but the deterministic inverse tools available at the time did not permit quantitative uncertainties – on the conductive anomaly itself, the requirement for partial melt, the degree of partial melt, or the degree of mantle hydration. Bayesian inversion of data from two magnetotelluric sites confirm that the conductor is indeed robust, and that melt is required by nearly 100% of the models that fit the data. Further, the resistivity uncertainty from the Bayesian inversion is used in conjunction with petrological modeling of partial melting in the mantle and an estimated probability distribution for temperature to place constraints on the degree of partial melt and mantle volatile (water and carbon) inventory over the depth range 45-63 km. This analysis concludes that large melt fractions and either high temperatures or a high degree of mantle hydration are likely needed to explain the resistivities produced by the Bayesian inversion, potentially explaining the mechanism for plate sliding that enables plate tectonics. Finally, chapter 5 introduces 2D trans-dimensional Bayesian inversion of magnetotelluric data, for the first time to my knowledge. A Gaussian Process-parametrized, trans-dimensional Markov chain Monte Carlo algorithm is used with MARE2DEM to invert synthetic data as well as field data from the Gemini data set from the Gulf of Mexico. For Bayesian inversion to be computationally feasible beyond inverting for 1D models, the cost of forward modeling must be reduced, as well as the number of model parameters that the algorithm must sample over. The first challenge is addressed through high performance computing. The forward modeling is performed on a cluster. In addition, we implement parallel tempering, where multiple Markov chains are run in parallel and swap models at each iteration, vastly increasing the rate at which the model space is explored and sampled. The curse of dimensionality is addressed by utilizing a Machine Learning technique known as a Gaussian Process to represent the model with far fewer parameters than required in a typical discrete finite difference or finite element representation of the subsurface. The Bayesian inversion of the Gemini data successfully recovers the model structure obtained by deterministic inversion of the same data, but additionally provides uncertainty on bulk resistivity. This thesis demonstrates the power and utility of Bayesian inversion to move beyond single estimates of subsurface resistivity. Not only does the work in this dissertation show that Bayesian inversion can provide uncertainty on inverted resistivity, it shows that these inverted uncertainties can be used to place quantitative constraints on parameters related to bulk resistivity. This is crucial to rendering the information obtained from inversion of electromagnetic data useful to disciplines far beyond electromagnetic geophysics.