In this article:
Overview
Welcome to VRIFY Predict and our AI-assisted mineral discovery platform DORA, your new tool for turning complex datasets into clear exploration insights.
DORA is designed to support geological decision-making by helping you surface patterns across large, multi-source datasets. By combining your geological understanding with machine learning–powered Prediction Maps, DORA helps you focus exploration efforts where they’re most likely to pay off.
While your data is being prepared behind the scenes, this guide will show you what to expect and how to get ready to run your first experiment. You'll learn how DORA works, what’s happening with your data, and how to start making confident, data-backed exploration decisions.
1. Understand What’s Happening With Your Data
Before you can run your first experiment, VRIFY’s team processes your data to ensure it's clean, aligned, and compatible with machine learning.
Learn more in DORA Onboarding and Key Milestones.
2. Explore What You Can Do With DORA
Once your data is processed, DORA lets you:
Run experiments to identify prospective exploration zones
Generate Prediction Maps enhanced by machine learning
Compare multiple experiments side-by-side for better decision-making
3. Learn the Key Concepts
While you don’t need to be a machine learning expert to use DORA, there are some key concepts that you should familiarize yourself with.
Start your knowledge journey with these articles:
These short articles build your understanding of how DORA interprets your data and creates Prediction Maps.
4. Ready to Run Your First Experiment?
Use our interactive walkthroughs to guide you through the Prediction Map workflow. Each step explains both the geoscience and AI logic behind it.
Create a Prediction Map
Select Area of Interest (AOI) – Define where you want DORA to analyze
Select Input Features – Choose which geoscientific data to include (e.g. geology, geochem, geophysics)
Set up Learning Data – Label known occurrences (positive/negative examples)
Embed Input Features – Transform your data into machine-readable formats
Build Predictive Model – Train the model to recognize patterns
Identify Targets – Review DORA’s prospectivity output
Interpret the Outputs
VRIFY Prospectivity Score (VPS) – Understand DORA’s confidence in each predicted zone
Feature Importance (SHAP Labels) – See which inputs influenced the model
Prediction Accuracy (Confusion Matrix) – Evaluate how well the model predicts mineralization
Depth Accuracy (R² Valid Scatter Plot) – Check vertical model performance with R² plots
Helpful Articles & Walkthroughs
Get Help With DORA
Reach out to your DORA contact. Not sure who? Check out this article for guidance.