In this article, you'll find a high-level overview of how DORA processes your geological data to generate a Prediction Map. Each stage in the workflow builds on the last, moving from raw data inputs to ranked exploration targets.

For a detailed breakdown of what happens when you run a prediction, see How DORA Generates a Prediction Map.

DORA processes geological data through a structured, repeatable workflow. Each stage builds on the last, moving from raw data inputs to ranked exploration targets.

Your project-specific vector and gridded data is uploaded to the Files Uploader, where it is standardized and unified for use across the platform. If available, public-domain datasets for the project area can also be included.

Selected input data passes through a proprietary data augmentation process, generating additional datasets that expand geological and spatial context.

The augmented layers are combined with the raw grids to create an exploration data stack — a unified set of inputs ready for modeling.

Data fusion is carried out using one of three kinds of deep learning foundation models:

Your Learning Data (known examples of mineralized and unmineralized locations) is used alongside the fused data to train a model that identifies mineralization patterns in your Area of Interest (AOI).

Learn more about DORA's foundation models.

A Prediction Map is generated for the Area of Interest (AOI), where mineral potential is expressed as a VRIFY Prospectivity Score (VPS). Higher VPS values indicate greater mineral potential.

High-VPS domains are clustered into targets to support exploration prioritization.

As new data becomes available, the process can be iterated to refine results.

Reach out to your dedicated DORA contact or email support@VRIFY.com for more information.