<p>Structural geomodeling is a key technology for the visualization and quantification of subsurface systems. Given the limited data and the resulting necessity for geological interpretation to construct these geomodels, uncertainty is pervasive and traditionally unquantified. Probabilistic geomodeling allows for the simulation of uncertainties by automatically constructing geomodels from perturbed input data sampled from probability distributions. But random sampling of input parameters can lead to construction of geomodels that are unrealistic, either due to modeling artefacts or by not matching known information about the regional geology of the modeled system. We present here a method to incorporate geological information in the form of geomodel topology into stochastic simulations to constrain resulting probabilistic geomodel ensembles. Simulated geomodel realisations are checked against topology information using a likelihood-free Approximate Bayesian Computation approach. We demonstrate how we can learn our input data parameter (prior) distributions on topology information in two experiments: (1) A synthetic geomodel using a rejection sampling scheme (ABC-REJ) to demonstrate the approach; (2) A geomodel of a subset of the Gullfaks field in the North Sea, comparing both rejection sampling and a Sequential Monte Carlo sampler (ABC-SMC). We also discuss possible speed-ups of using more advanced sampling techniques to avoid simulation of unfeasible geomodels in the first place. Results demonstrate the feasibility to use topology as a summary statistic, to restrict the generation of model ensembles with additional geological information and to obtain improved ensembles of probable geomodels using stochastic simulation methods.</p>