VRIFY's Feature Processing system generates hundreds of high-resolution raster layers per project. These outputs support DORA’s Prediction Maps and help exploration teams identify geological patterns and guide their decisions. In both exploration and AI applications, ensuring data quality and standardization is the most crucial first step — and validating that data is essential to building trust in the results. While the validation framework varies by Module, VRIFY applies a consistent set of checks and balances to ensure outputs meet both technical standards and geological expectations.

Projects typically produce hundreds of raster layers, reflecting both the depth of the input data and the granularity needed for thorough exploration analysis.

Each project requires a tailored approach. Parameters are tested, adjusted, and reviewed based on the context of the data and the geological objectives. Outputs are evaluated against known geological features, and Modules are fine-tuned through exploratory research and iterative review. The focus is on ensuring that the results are geologically sound based on available support data and input from the project team.

For data extrapolation grids, such as those produced by geochemical or prediction Modules, VRIFY’s team closely reviews R² (R-squared) graphs, providing a statistical check on how well the model fits the known data (in validation datasets), helping to determine whether the resulting grid can be trusted for decision-making. In other cases, generated feature layers are marked as “LowQC”, indicating that the interpolation accuracy is lower or that the input data is of lower reliability. These “lowQC” features can still be valuable for analyzing key exploration targets and are included for completeness.

VRIFY’s geoscience experts have optimized default parameters for each of the Feature Processing Modules, but refinement is central to producing meaningful results. Visual inspections, adjustments to processing parameters, and cross-validation with existing datasets are standard steps. For example, structural and geophysical outputs are visually compared to expected geological trends, and geochemical outputs are reviewed to ensure anomalies align with sample distributions.

Engagement with the project team is a critical part of validation. VRIFY's geoscientists track all inputs, parameters, and outputs internally, while also working closely with exploration teams to ensure transparency around how results are generated and supported.

This includes collaborative reviews of the outputs, clarifying how field samples, public data, and processing workflows contribute to each result, and providing context for any anomalies or unexpected features. Input from the project team helps confirm that the data is geoscientifically sound and consistent with expectations based on their original datasets. When needed, outputs are refined based on feedback to ensure the final results are well-understood and trusted.