<p>Increasing temperatures and extreme weather events driven by climate change have heightened the risk of rockfall hazards. Reliable estimates of rockfall hazards have a significant impact on the design of roadways, slopes, open pits, and underground excavations. The reliability of these estimates relies on numerical models calibrated using rockfall catalogs. Recent advancements in radar technology enable high-resolution monitoring of rockfall events, providing valuable data on their propagation. However, there are few methods to back-analyze numerical model parameters from radar data. Hence, we propose a Bayesian Optimization Framework to address this gap and demonstrate the framework through a Doppler-radar-tracked rockfall catalog comprising 21 events and 19,356 rock positions. Specifically, we use Gaussian Process Regression (GPR) as a surrogate model to minimize the number of simulations required to determine the best-fitting coefficients of restitution (CORs). The framework supports event-based and site-wise analysis. The former can readily identify unmatchable low-quality events, and the latter excels in fitting CORs generalizable across multiple events. Our framework achieves comparable resolution using five times fewer simulations compared to a benchmark grid-search method. Trajectories simulated using the mean and covariance of the best-fit CORs exhibit reasonable agreement with the measured data. This framework accelerates data inspection and parameter searching when back-analyzing numerical model parameters using a catalog of multiple rockfall events.</p>