Graph neural network-based topological relationships automatic identification of geological boundaries

Abstract

Topological relationship identification of geological boundaries greatly affects 3D geological modeling efficiency. However, due to the complex spatial distribution and unstructured data architecture of geological boundaries, the current topological identification still mainly depends on the manual operation and interpretation, which is time-consuming and labor-intensive. And geologists' judgments are based on knowledge and experience that are challenging to replicate through conventional algorithmic approaches. To address these challenges, a graph neural network-based geological boundary topological relationships automatic identification method is proposed. Based on graph theory, we conceptualize geological boundaries and their topological relationships as a graph transforming the identification task into an edge prediction problem within this topological graph. We employ the variational graph auto-encoder (VGAE) model to predict unknown edges in the topological graph, enabling automatic identification of geological boundary topological relationships. A geological information transformation method is proposed to extract and encode the geological boundaries’ features as inputs for VGAE. The proposed method is verified in an engineering case study. The results reveal that this method can efficiently and accurately analyze the geological boundary topological relationships. Moreover, it can greatly reduce the manual workload, requiring only 30% of the manual effort to obtain the geological boundary topological relationships, while achieving an average prediction accuracy of approximately 95%.