RINGral is a python library for the computation of the probabilities that several fault observations belong to the same fault.

This work uses Random Forest learner trained from a set of selected fault features computed from fault traces extracted from known 3D geological models (e.g., the length of the fault trace, the throw value, etc.). The association likelihood inference is formulated as a classification problem to determine the probability that fault observations belong to the same fault object based on the variables computed from the features of the k observations. For now, the application is limited to a partly interpreted case: we split the 3D domain in two disjoint, spatially contiguous sectors A and B, and use sector A as training and sector B for testing. The results are shown in a graph view were all the observation correspond to one node and the edges the association probabilities.