Overview

When setting up a DORA Prediction Map, you will be guided through six key steps to analyze geological data and other relevant features to generate results.

In this article, you'll find links to interactive walkthroughs for each step of the DORA Prediction Map workflow. For deeper context, including geoscience rationale, AI model behavior, and practical tips, we recommend reading the full step-by-step articles linked alongside each walkthrough.

Introduction to DORA
Step 1: Select Area of Interest
Step 2: Select Input Features
Step 3: Set Up Learning Data
Step 4: Embed Input Features
Step 5: Build Predictive Model
Step 6: Identify Targets

Introduction to DORA

As a starting point, review this step-by-step introduction to the DORA interface. You’ll learn how to navigate the interface and tools available when creating a Prediction Map.

🔗 Read the full article: Introduction to DORA.

Step 1: Select Area of Interest (AOI)

Learn how to define your Area of Interest (AOI) when creating an experiment in DORA.

Setting the AOI narrows the focus of your analysis by limiting the features and learning points to a specific geographic boundary.

🔗 Read the full article: Step 1: Select Area of Interest (AOI).

Step 2: Select Input Features

In this step, you choose the geoscience data you want to include in the experiment.

Additionally, you can adjust features, visualization options, and correlation thresholds, allowing you to highlight specific data layers and emphasize the details most relevant to your experiment.

🔗 Read the full article: Step 2: Select Input Features.

Step 3: Set up Learning Data

In this step, you'll select your target elements and set thresholds to classify your learning points. Learning data defines which elements you're targeting and separates sample points into positive (mineralized) and negative (unmineralized) examples.

These labeled points train the model to recognize patterns in your dataset and make accurate predictions.

🔗 Read the full article: Step 3: Set Up Learning Data.

Step 4: Embed Input Features

In this step, you’ll select a Data Fusion model and set the number of training epochs to embed your input features.

This process reduces the complexity of your data while tailoring the model to your specific geological setting.

🔗 Read the full article: Step 4: Embed Input Features.

Step 5: Build Predictive Model

In this step, DORA compares your embedded input data to clustered learning points, which are grouped for training and validation.

This process drives the supervised learning model that predicts the likelihood of mineralization across your AOI, directly influencing accuracy and generalization.

🔗 Read the full article: Step 5: Build Predictive Model.

Step 6: Identify Targets

In this step, you’ll define and visualize your final exploration targets using the VRIFY Prospectivity Score (VPS) generated by your predictive model.

You'll adjust threshold and clustering settings to group high-prospectivity zones into distinct targets, then evaluate them using your geological knowledge and project context.

🔗 Read the full article: Step 6: Identify Targets.