Towards a self-consistent crustal thermal model of the continental U.S.: a Monte Carlo approach

Crustal thermal structure provides fundamental information to interpret seismic structures and to constrain other crustal properties such as density, rheology, and seismic attenuation, and, in aggregate, parameters that impact earthquake source properties, ground motions, and seismic hazard. Crustal temperature estimates, however, suffer from high variability, partially because they can be informed by independent geophysical and geological methods that can be difficult to reconcile (e.g., Boyd, 2019). To construct a self-consistent thermal model of the U.S. continental crust, we integrate multiple thermal data sets via a Monte-Carlo algorithm. These data sets include direct, in-situ observationssurface temperature, geothermal heat flux, and near-surface heat conductivity and heat generationand indirect constraintsMoho temperature from Pn tomography (e.g., Perry, 2006; Schutt et al., 2018), Curie depth from geomagnetic data, and crustal heat generation estimated from the latest compositional model derived from the USArray (Sui et al., 2021). Using a simple model parameterization, we produce a vertically smoothed 3-D thermal model compatible with these geologically- and geophysically-determined constraints. The resulting thermal model presented here better constrains crustal density and rheology models for the continental US and will improve calibration of future compositional models.