Bayesian Inversion of Rate-and-State Parameters from Stable Velocity Step Experiments on Quartz Gouge Close to Instability

Summary Accurate determination of rate-and-state friction parameters from experiments is crucial for physics-based modeling of the earthquake source. In this study, we develop a Bayesian inversion framework based on a spring-slider model of the experiment to infer rate-and-state frictional properties from stable velocity-step experiments of a fine-grained quartz gouge that is close to instability. The inversion framework infers frictional parameters with superior precision to conventional methods based on non-linear least squares. We identify that logarithmic sampling of the characteristic slip DRS is an essential step for accurate inversion. Testing the same gouge material under the same effective normal stress but different pore pressure additionally shows that the presence of pressurized pore fluids changes the inferred rate-and-state properties of the gouge material by increasing rate-and-state parameters a, b, and DRS. The Bayesian inversion framework provides a valuable tool for inferring accurately rate-and-state parameters of realistic gouge materials close to instability, with a small and quantifiable range of uncertainty in cases where the steady-state behavior quantified by a − b can be constrained independently. We also find that the steady-state friction properties evolve over the course of the experiment, adding additional uncertainty to the rate-and-state parameter estimates but not altering the conclusion about significant differences between the rate-and-state parameters in the presence of fluids.