 data assimilation has been used to estimate the current and future states of a fault given uncertain parameters. It was found that a small change in the parameter could have a significant effect on the estimated states. Increasing the particle spread by accounting for model error and an additional resampling step increased the accuracy of the estimates. However, when there was a large parameter bias, only state parameter estimation could fully account for the parameter bias. This shows the potential of data assimilation for the estimation of earthquake sequences and provides insight into its application in other non-linear processes with uncertain parameters. This article was authored by a Banerjee, Y. Van Dinder, and F. C. Vosipel.