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Regolith, or unconsolidated materials overlying bedrock, exists as an active zone for many geological, geomorphological, hydrological and ecological processes. This zone and its processes are foundational to wide-ranging human needs and activities such as water supply, mineral exploration, forest harvesting, agriculture, and engineered structures. Regolith thickness, or depth-to-bedrock (DTB), is typically unavailable or restricted to finer scale assessments because of the technical and cost limitations of traditional drilling, seismic, and ground-penetrating radar surveys. The objective of this study was to derive a high-resolution (10 m2) DTB model for the province of New Brunswick, Canada as a case study. This was accomplished by developing a DTB database from publicly available soil profiles, boreholes, drill holes, well logs, and outcrop transects (n = 203,238). A Random Forest model was produced by modeling the relationships between DTB measurements in the database to gridded datasets derived from both a LiDAR-derived digital elevation model and photo-interpreted surficial geology delineations. In developing the Random Forest model, DTB measurements were split 70:30 for model development and validation, respectively. The DTB model produced an R2 = 92.8%, MAE = 0.18 m, and RMSE = 0.61 m for the training, and an R2 = 80.3%, MAE = 0.18 m, and RMSE = 0.66 m for the validation data. This model provides an unprecedented resolution of DTB variance at a landscape scale. Additionally, the presented framework provides a fundamental understanding of regolith thickness across a post-glacial terrain, with potential application at the global scale.

A High-Resolution, Random Forest Approach to Mapping Depth-to-Bedrock across Shallow Overburden and Post-Glacial Terrain