A quantitative framework for subsurface uncertainty assessment using multidisciplinary geoscientific data

This study introduces the Integrated Model Coherence Index (IMCI), a novel conceptual framework for quantifying the reliability of multi-method subsurface geoscientific models. While joint inversion techniques focus on identifying a best-fit model that minimizes global error, they often show disagreements between independent datasets. The IMCI addresses this limitation by focusing on interpretational convergence, measuring how different data streams (geological/remote sensing, geophysical, and geochemical/borehole) predict the same subsurface properties, thereby transforming qualitative, multidisciplinary observations into a quantitative, risk-based metric. The output is a 3D confidence cube, in which each voxel is assigned a normalized reliability score. High-confidence zones indicate mutual reinforcement between data streams, while low-confidence areas highlight ambiguity. This can guide targeted data acquisition and help de-risk exploration in fields such as natural resource exploration and management, as well as hazard assessment.