Bayesian inversion of surface heat flow in subduction zones: A framework to refine geodynamic models based on observational constraints

Abstract

Surface heat flow has been widely used to constrain the thermal structure of subduction zones. However, the forward modelling approaches in previous geodynamic studies have only provided limited information on the model parameters controlling the thermal structure, which makes model validation difficult. Here we apply a probabilistic inversion technique based on Bayes' theorem to surface heat flow data from Tohoku in Japan and Cascadia to simultaneously infer five model parameters that appear to have the greatest influence on the thermal structure of subduction zones. The surface heat flow is predicted via 2-D steady-state thermomechanical modelling. The Metropolis algorithm is used to obtain the posterior probability distributions. A comparison of our results with previous estimates indicates that our activation energy for the shear viscosity of dislocation creep is lower in both regions, and our radiogenic heat production rate in the upper continental crust is lower in Cascadia. These findings suggest that our geodynamic models cannot explain the surface heat flow observations with the acceptable ranges of model parameter values. We therefore need to refine the models by including, for example, the effects of recent backarc extension, vigorous thermal convection beneath the overriding plate and fluid circulation in the uppermost part of the oceanic crust. The approach presented here also allows us to determine trade-offs between the parameters. This study provides a framework to validate and refine geodynamic models based on various types of observations.