Assessing the traversability of rugged terrain is a difficult challenge for legged robots, especially when they implement multiple, distinct gaits. We tackle this problem on the k-rock2 amphibious, sprawling gait robot by training a gait-dependent traversability estimator. We verify that the estimator, trained solely on procedurally-generated simulated data, approaches the outcomes of real-world experiments conducted in an indoor motion capture arena using two distinct terrestrial gaits to cross various indoor obstacles. In simulation experiments on a large-scale outdoor heightmap representing real-world data, we quantify the performance gain using the estimator outputs for gait selection. Further, we apply the method to heightmaps of outdoor data to illustrate how the approach could readily he applied to field scenarios.