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[DORA 2.0] How to Interpret The VPS on a Prediction Map

Learn how to work with the VPS and Target Groups.

Updated today

Overview

Once your Prediction Map is generated, the VRIFY Prospectivity Score (VPS) helps you identify and evaluate areas of potential mineralization across your AOI.

This article explains how to interpret VPS values and use them to define and refine exploration targets.


Interpreting VPS Values

The VPS is a probability score ranging from 0 to 1.0, displayed as a heatmap across the AOI. Higher values indicate stronger similarity to known mineralization patterns, while lower values indicate weaker similarity.

As a general guideline:

  • Above ~0.5 → potentially mineralized

  • Below ~0.5 → lower prospectivity

These thresholds are not fixed and should be interpreted in the context of your project and data.


Adjusting the Target VPS Threshold

Use the VPS threshold slider in the lower left of the map to control which areas are highlighted. You can switch between the Surface VPS and Sub-surface VPS:

  • Slide right → focus on higher-confidence targets

  • Slide left → include broader areas of potential

This allows you to move between selective targeting (area of higher confidence) and exploratory analysis (broader coverage).

Surface VPS adjustment


Using the VPS with Target Groups

Use the Create Target Groups tool to organize high-VPS areas into clusters.

This helps you identify coherent zones of prospectivity, organize targets across your project area, and understand which features are driving each group.

You can adjust grouping parameters to match your project scale and exploration strategy.

Target Groups on Prediction Map


Comparing VPS Results

Use the Compare Experiments tool to evaluate how VPS results change across different model configurations.

This helps you test geological hypotheses, compare feature selections, and assess the impact of different inputs.

Compare Experiments


Tips & Considerations

  • VPS reflects similarity to known patterns, not certainty of mineralization

  • Results depend on the quality and quantity of your input data

  • Different configurations may produce different results

  • Iterating on experiments can help refine targets and improve understanding


Learn More


Still Have Questions?

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

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