Overview

In this step, you select the raster input features DORA will use to identify spatial patterns associated with mineralization.

Input features may include geophysical data, geochemistry grids, geology maps, structural data, or outputs from Data Augmentation modules. These rasters provide the spatial signals the model uses to generate a Prediction Map and calculate the VRIFY Prospectivity Score (VPS).

What’s Improved

This step now includes automatic raster scoring and feature recommendations.

Instead of manually reviewing correlations or tuning model parameters, DORA evaluates each raster for quality, relevance, resolution, and redundancy. A recommended feature set is generated automatically, helping you move from setup to results quickly.

How DORA 2.0 Evaluates Input Features

Each raster is evaluated using five data-quality and relevance checks. The final score reflects its overall suitability for the selected AOI and deposit model. Each criterion is weighted equally, and the final score is calculated as the average of all scores.

Features are colour-coded based on score, making it easy to see which rasters are most strongly recommended.

Recommended input features are automatically selected, giving you a strong feature set as a starting point. However, we recommend reviewing and adjusting as needed based on your geological understanding.

Learn how DORA 2.0 scores input features based on five data-quality and relevance checks.

How Input Features Are Organized

Input features are organized into six categories for easy review:

Data Augmentation: Rasters generated using VRIFY’s Data Augmentation Modules, which transform raw exploration data into enhanced, continuous input layers for modeling.
Geology: Geology rasters, including those created using the Distance Maps and Fault Disturbance Maps Data Augmentation Modules.
Geochemistry Maps: Geochemical raster layers.
Geophysics: Geophysical raster layers.
Satellite: Satellite-derived raster layers.
Uncategorized: Rasters that do not fit into the categories above.

Understanding Advanced Settings

The Prediction Tendency setting controls how selective the final Prediction Map will be. It determines how closely new areas must resemble your positive learning points to be highlighted. The default Balanced setting is suitable for most use cases.

The Resolution setting defines the spatial scale at which the model evaluates your data across the selected AOI.

Learn more about Advanced Settings: Prediction Tendency and Resolution.

Step by Step Instructions

Open Select Input Features
- From the experiment setup panel, click Step 4: Select Input Features.
Review Recommended Features
- DORA automatically displays a pre-selected, recommended feature set.
- Features are grouped by category:
- Next to each feature, you will see:
  - A recommendation score (0–100).
  - A corresponding color indicator for quick review.
- Hover over the feature on the far right to open Feature Details (AOI, resolution, and other metadata).
- To adjust how a raster appears on the map:
  - Click the eye icon on the left to display the raster on the map:
  - Adjust Colors and Transparency to highlight key details:
- (Optional) Modify Feature Selection
  - Recommended features are a strong starting point, but should always be reviewed and adjusted based on your geological understanding.
  - Click the Recommended Selection slider to turn off automatic selections:
  - Manually check or uncheck features to create a custom feature set.
- (Optional) Adjust Advanced Settings
  - Click the settings icon in the top right to open Advanced Settings:
  - (Optional) Change Resolution
    This will clear the current input feature selection and rerun the previous step.
    Click Apply to save your changes and rerun the previous step. Note the warning:
  - (Optional) Select Prediction Tendency
    All experiments are automatically set to the default Balance setting. Select Conservative or Aggressive from the drop-down, based on your exploration strategy:
    Click Apply to save your changes.
Generate Prediction
- Click Run Prediction to run the model and create your Prediction Map.
- This step takes approximately 30 minutes to complete.

Considerations When Selecting a Custom Feature Set

The automated recommendations provide a strong, data-driven starting point. However, you may choose to modify input features for several reasons:

A recommended raster does not align with your geological understanding
You want to test how a specific raster (even with a low score) influences results
You want to run a targeted experiment using a specific data category (e.g., geology-only)

Modifying inputs is particularly useful for hypothesis testing and follow-up experiments.