Novel graph convolutional network for geological profile prediction using non-equidistant borehole data

Accurately predicting geological profiles from sparse borehole data is challenging due to limited data availability and complex spatial correlations within strata. This study presents an advanced graph convolutional network designed to model flexible graph structures and predict geological profiles using unequally spaced borehole data. In this framework, each graph node corresponds to a stratum unit which represents the transformed coordinate features, with edges indicating spatial relationships among these units. Initially, the method was applied to a case study in Australia, demonstrating a 30% improvement in boundary accuracy compared to traditional methods. Subsequently, the approach was employed in a foundation project in Hangzhou, achieving mean measurement accuracies above 0.8 for validating boreholes. The study further explored the impact of borehole quantity and unequal spacing on prediction accuracy. Results indicate that significant accuracy gains occur only when new boreholes add critical spatial features; otherwise, the improvement remains minimal. Additionally, flexible borehole positioning results in a 44% disparity between the most and least accurate predictions.