Overview
This step is where your exploration targets are generated from the prospectivity score and then visualized. The parameters in this step are used to refine the scope and precision of your exploration targets.
We aim to cluster targets together because different data types influence predictions differently from one zone to another. For example, targets in the north might be driven by geophysical anomalies, while those in the south could be influenced by proximity to a fault. By clustering these targets, we can more accurately analyze and evaluate the results.
In this step, your industry knowledge and familiarity with your project to evaluate the results is critical, ensuring the results fit within established geological patterns and project specifics for informed decision-making.
The view panel will show a range of shades which visually represent the Verified Prospectivity Scores (VPS). Prospectivity is represented by this colour scale:
Read on for more context and explanations about what this step entails.
Key Concepts by Parameter
Parameter: Target Threshold
This setting allows you to control the probability threshold of the prospectivity scores that are shown in the view panel. By adjusting the slider, you can choose which areas to highlight based on their prospectivity scores. This gives you flexibility in analyzing regions based on the level of exploration potential you're targeting.
Moving the slider to the right will filter the display to show only the areas with the highest prospectivity scores, focusing on the regions most likely to be of interest.
Moving the slider to the left will broaden the display to include areas with moderate prospectivity scores, giving you a more comprehensive view of potential prospects.
Keep in mind that values below 0.5 should be considered barren ground (or negative predictions) and values above 0.5 should be considered potential mineralized ground (or positive predictions).
Parameter: Minimum Cluster Size
This setting controls the minimum cluster size needed to be considered a distinct target. By adjusting this, you influence how close or far apart points must be to form a target.
Increasing the minimum cluster size groups points that are further apart into targets, resulting in fewer, larger targets.
Decreasing the minimum cluster size allows closer points to be considered part of the same target, capturing more, smaller targets.
Parameter: Minimum Samples
This setting specifies the minimum number of predicted points required to form a cluster that qualifies as a target. Adjusting this value determines how many points are needed before the model considers the cluster a valid target.
Increasing the minimum sample count makes it harder to form a target, requiring more data points to create clusters, focusing on larger and more significant targets.
Decreasing the minimum sample count allows smaller groups of points to be classified as targets, identifying smaller potential exploration areas.
For information on how to interpret this step's results graphs, check out this article.
Still have questions?
Reach out to your dedicated VRIFY AI Contact or email Support@VRIFY.com for more information.