Quantifying uncertainties in the inference of lithospheric heat flow

Heat flow is a primary driver of lithospheric strength and geodynamics. However, it cannot be measured directly and must be inferred from detailed borehole studies. The consequence of this is that the quality of individual heat flow determinations varies widely, while large geographical regions remain sparsely and unevenly sampled. Spatial patterns in lithospheric heat flow can be predicted through the application of geophysical inverse theory. In particular, trans-dimensional Bayesian methods allow candidate models to be sampled while simultaneously solving for heterogeneous model complexity and the magnitudes of data noise. This is essential for both rigorous model predictions, and for quantification of associated prediction uncertainty. In this presentation we describe the development and application of these methods to geothermal research through their application to infer the conductive heat flow through the Australian continental lithosphere. In particular, we describe how marginalisation can be used to account for limitations in the forward model, enabling inference even when key model parameters may not be empirically predicted. Presented at the 2018 American Geophysical Union (AGU) Fall Meeting