Deep learning-aided interpretation of regional lake sediment geochemical surveys: A case study from the Hackett River zone, Nunavut, Canada

The Hackett River zone, located in the northeastern part of the Slave Geological Province, Nunavut, Canada, is a prolific metallogenic zone hosting two principal styles of mineralization: volcanogenic massive sulfide (VMS) and banded iron formation (BIF)-related gold mineralization. The region has been the focus of both legacy and modern lake sediment geochemical surveys, with the modern program involving the reanalysis of archived samples for an expanded suite of elements. Despite the modernization of this geochemical database, delineating mineralization-related anomalies associated with the distinct mineralization styles remains challenging. The first challenge arises from the multivariate and compositional nature of regional geochemical data, which requires appropriate statistical treatment to address closure effects and accurately identify meaningful multi-element geochemical associations. The second challenge relates to the geological complexity of the study area, characterized by heterogeneous lithologies, deformation patterns, and overlapping geochemical signatures that complicate conventional anomaly detection approaches. To address these challenges, this study reformulates geochemical anomaly delineation as a bagging-based positive–unlabeled learning problem integrated with compositional data analysis, deep convolutional neural networks, generative adversarial networks, and large language models. An improved cross-validation framework was further implemented to rigorously evaluate the robustness of the generated anomaly maps. The resulting geochemical anomaly maps successfully reduce the exploration search space and demonstrate strong spatial correspondence with the distribution of favorable bedrock units and known mineralized trends, indicating that the proposed framework is effective for the interpretation of regional lake sediment geochemical survey datasets.