Speaker: Amandine Fratani

Date: Thursday 11th of April 2024, 1:15pm.


Interpretation of faults is a requirement for a 3D geological modelling process. However, due to the incomplete observations caused by the gap between 2D seismic images or outcrops, the results of this stage can be ambiguous and uncertain. Recently, a proposition of solution based on a graph formalism has been expressed. In the graph, fault observations are represented as nodes, and edges carry the potential of pairwise associations computed from selected expert geological rules. Main limit of this work is that fault observations are not pairwise independent, therefore considering pair prevents the consideration of higher-order effects such as the distribution of the throw along several aligned nodes. We propose to consider a multiple-point likelihood computation to extend the graph where expert rules are replaced using machine learning on analog or partly observed data. The model is trained from a set of selected features such as the length of the fault trace or the throw value. Features are computed from fault traces extracted from 3D geological models. Association potential of k fault observations are then directly computed using the trained model. To prevent overfitting in our small geological models dataset, we propose to mimic a partly interpreted case: we split a 3D domain in two disjoint, contiguous sectors A and B, and use sector A as training and sector B for testing. This presentation will show first results on 2 and 3 points association using a Random Forest algorithm.