The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature

Abstract

SUMMARY Volgo–Uralia is the easternmost segment of the East European Craton. It accommodates the Volga–Ural petroleum province where the maturity of source rocks is tightly related to the temperature distribution in the crust. Numerous heat flow and temperature measurements have been reported for this region. However, no consistent geothermal model was presented for the Volgo–Uralian crustal block so far. In this study, we present a novel model of the Volgo–Uralian geothermal field where we aim to reconcile the reported heat flow and temperature data. The main goal of the study is to explore lateral variations of the unknown thermal parameters within Volgo–Uralia. For this purpose, we applied a Bayesian Markov Chain Monte Carlo approach where we used the known surface heat flow, surface temperature, lithosphere–asthenosphere boundary temperature and thicknesses of the Earth's lithospheric and crustal layers as input and investigated the possible lateral variations of crustal and lithospheric mantle thermal conductivities, crustal heat production and mantle heat flow. We implemented this methodology for a single-layer and multilayer crust and validated the obtained geothermal models with existing subsurface temperature measurements for the region. The results show that the Volgo–Uralian subcraton is characterized by significant lateral variations of crustal radiogenic heat production (RHP) and mantle heat flow. The variations of crustal and lithospheric mantle thermal conductivities are less pronounced. According to our model, the surface heat flow distribution is controlled mostly by crustal RHP which accounts for more than half of Volgo–Uralian surface heat flow. Validation of the models shows that single-layer and multilayer crustal models give roughly the same fit of measured and modelled temperatures. This implies that a single-layer crust with constant RHP can be considered a sufficient approximation for regional-scale geothermal modelling.