VRIFY Technology Inc.

Guides and resources for VRIFY's exploration intelligence suite, including DORA and Data Augmentation

VRIFY Predict

DORA

Learn what to expect after onboarding and how to create your first Prediction Map with DORA.

Getting Started With VRIFY Predict

A short demo video highlighting VRIFY's DORA.

DORA: 3 Minute Demo Video

Learn about the key differences between DORA 1.0 and DORA 2.0

What's New in DORA 2.0

Learn how existing DORA 1.0 experiments behave after the release of DORA 2.0, including how to access, edit, and duplicate older experiments.

Working With DORA 1.0 Experiments After the DORA 2.0 Release

Learn how to navigate the DORA interface, including the top navigation bar, map panel, and experiment controls.

Introduction to the DORA Interface

Learn how to manage experiment visibility using My Drafts and Published Experiments in DORA.

Understanding Drafts and Published Experiments

Learn how to use Cliff, DORA's AI assistant, to get answers to your questions about DORA features and workflows without leaving the platform.

Using the DORA Chatbot

Rotate, zoom, and pan the DORA Prediction Map map, and jump to a standard view with keyboard shortcuts.

Use Mouse and Keyboard Controls in DORA

Getting Started With DORA

Understand the basics of Artificial Intelligence (AI).

Introduction to Artificial Intelligence (AI)

Learn how DORA compiles and processes geological data to generate a Prediction Map and support exploration decision-making.

How Does DORA work?

Definitions of DORA frequently used terms.

DORA Glossary of Terms

Understand what overfitting and underfitting means for an AI model.

Overfitting and Underfitting in Machine Learning

Learn what foundation models are, why DORA uses them instead of standard machine learning models, and how to choose between deposit-specific and deposit-agnostic options.

Foundation Models: What They Are and Why They Matter

Learn which foundation models are available in DORA, including the deposit types and target commodities each model is trained to identify.

Available Foundation Models

How DORA works, key concepts, and glossary

Understanding DORA

Learn how to run an experiment and create a Prediction Map in DORA.

Create a Prediction Map

Learn how to set your Area of Interest when creating an experiment in DORA.

Step 1: Select Area of Interest (AOI)

Learn how to define mineralized and unmineralized examples using data-driven threshold recommendations to train your DORA experiment.

Step 2: Set Up Learning Points

Learn how to choose the foundation model that aligns with your target mineral system.

Step 3: Select Deposit Type

Learn how to choose the raster layers that will be used to generate your Prediction Map.

Step 4: Select Input Features

Learn how to create discrete target clusters across your AOI.

Create Target Groups

Learn the steps to upload and delete your own AOI into a DORA.

Upload and Manage a New AOI

Learn how to drag and drop your own Learning Points file into the DORA interface.

Upload and Manage New Learning Points

Learn how and when to adjust Resolution when generating a Prediction Map.

Advanced Setting: Resolution

Learn how DORA automatically evaluates and ranks raster inputs in Step 4: Select Input Features, including how composite scores are calculated, what each criterion measures, and how AOI coverage is…

How DORA Scores and Ranks Input Features

Learn how to apply geological judgment alongside DORA's recommendations to build an effective input feature set.

Best Practice: Input Feature Selection Workflow

Learn how rasters are named and categorized in DORA.

How Input Features Are Named

Learn what causes this error, why it stops your prediction early, and how to resolve it.

Troubleshoot: “Select Input Features Failed” Error

Building a Prediction Map

Learn what the VRIFY Prospectivity Score (VPS) is and how to view the Surface and Sub-surface VPS outputs on a Prediction Map.

What Is the VRIFY Prospectivity Score (VPS)?

Learn how to read VPS values, adjust the threshold slider, review results with Learning Points, and generate targets on your Prediction Map.

How to Interpret the VPS on a Prediction Map

Learn how to read and interpret Feature Importance graphs from your DORA Prediction Map.

Feature Importance

Learn how to read and interpret the Prediction Accuracy, F1, and the Performance Breakdown matrix.

Prediction Accuracy and Performance Breakdown

Learn how to interpret the Depth Accuracy gauge and Real vs Predicted scatter plot to assess model performance in predicting mineralization depth.

Depth Accuracy and Real vs Predicted Scatter Plot

Behind-the-scenes overview of the processing steps that generate the DORA Prediction Map.

How DORA Generates a Prediction Map

Learn how DORA uses spatial cross-validation to train and evaluate its predictive model.

Train-Test Clusters in DORA

Learn how to add a note to your DORA Prediction Map to provide additional context to viewers.

Add a Note to a Prediction Map

Learn how to lock or duplicate a DORA experiment to prevent accidental edits or submissions.

Lock an Experiment

Learn how to cancel an active processing step in the Prediction Map workflow.

Stop Processing

Learn how to export selected Input Features from a DORA  experiment as a downloadable .zip file.

Export Input Features

Learn how to make a copy of a DORA Prediction Map.

Duplicate a Prediction Map

Learn about how to export and utilize the results from a DORA Prediction Map.

Export Prediction Map Results

Learn how to compare multiple VRIFY Prospectivity Scores (VPS) on a single Prediction Map.

Compare VPS Results on a Single Prediction Map

Learn how to view and interpret VPS uncertainty, review the Prediction Uncertainty layer, and use the VPS x Uncertainty filter to filter your Prediction Map by prospectivity and prediction uncertainty.

Using the VPS x Uncertainty Filter and the Prediction Uncertainty Layer

Learn how to interpret and apply the results from a DORA Prediction Map export.

Working With DORA Export Files in GIS and Other Tools

Learn how to use the Compare & Merge tool in DORA to evaluate multiple experiments side by side and combine them into a single Merged VPS.

Compare and Merge Experiments

Learn how to compare Conservative, Balanced, and Aggressive prediction tendencies in Prediction Results to identify the best targets for your exploration objectives.

Comparing Conservative, Balanced, and Aggressive Prediction Tendencies

Analyse, export, and compare your Prediction Map outputs

Interpreting Prediction Map Results

Information on preferred data types, formats, and applications for DORA.

DORA Data Requirements

Learn how fault and geological interpretation vectors are used in DORA.

Fault and Geological Interpretation Vectors

Learn how DEM grids and topographic surfaces are used in DORA.

DEM Grids and Topographic Surfaces

Learn how satellite imagery is used in geoscience and DORA.

Satellite Imagery (Aster and Sentinel 2)

Learn how drill core geochemistry is used in DORA.

Drill Core Geochemistry

Learn how surface geochemistry is used in DORA.

Surface Geochemistry

Learn how geological structural measurements are used in DORA.

Geological Structural Measurements

Learn how drillhole data (including lithology and assays) is used in DORA.

Drillhole Data - Lithology and Assays

Learn how geophysics data is used in DORA.

Geophysics Data

Reference guides for geological data in mineral exploration

Understanding Exploration Data

[DORA 1.0] What is a Prediction Map in DORA?

Learn how to understand a DORA Prediction Map with View-Only access.

[DORA 1.0] Understanding a Prediction Map (View-Only Access)

Learn how to access, navigate, and begin working with DORA to create your first Prediction Map.

[DORA 1.0] Introduction to the DORA Interface

Learn how to train DORA to make targeted mineralization predictions.

[DORA 1.0] Create a Prediction Map in DORA

[DORA 1.0] Step 1: Select Area of Interest (AOI)

Learn how to define the geoscience data used in your DORA experiment.

[DORA 1.0] Step 2: Select Input Features

Learn how to configure positive (mineralized) and negative (unmineralized) examples to train the model for your DORA experiment.

[DORA 1.0] Step 3: Set Up Learning Data

Learn how to select a Deposit Type Model to embed your input features in your DORA experiment.

[DORA 1.0] Step 4: Apply Data Fusion

Learn how to configure your Predictive Model in your DORA experiment.

[DORA 1.0] Step 5: Build Predictive Models

Learn how to define and visualize your exploration targets using the VRIFY Prospectivity Score (VPS).

[DORA 1.0] Step 6: Identify Targets

Learn about Advanced Feature settings in the Build Predictive Model step of a DORA experiment.

[DORA 1.0] Advanced Features for Building Predictive Models

Interpret results in Step 6: Identify Targets and learn how to improve outcomes.

[DORA 1.0] Interpreting DORA’s Outputs

Learn how to interpret one of DORA’s core outputs on a Prediction Map.

[DORA 1.0] What is a VRIFY Prospectivity Score (VPS)?

[DORA 1.0] Prediction Accuracy and Performance Breakdown

[DORA 1.0] Depth Accuracy and Real vs Predicted Scatter Plot

[DORA 1.0] Feature Importance (SHAP Labels)

[DORA 1.0]  Export Prediction Map Results

[DORA 1.0] Compare Experiments

Learn how to export selected Input Features from a DORA experiment as a downloadable .zip file.

[DORA 1.0] Export Input Features

[DORA 1.0] Lock an Experiment

Learn how to create a shareable link for view-only access to your DORA experiment.

[DORA 1.0]  Create a View-Only Share Link for a Prediction Map

Learn the steps to upload your own AOI into a DORA Prediction Map.

[DORA 1.0]  Upload a New Area of Interest (AOI)

[DORA 1.0] Upload New Learning Points

Learn how to add a summary to your DORA Prediction Map to provide additional context to viewers.

[DORA 1.0] Add a Summary to a Prediction Map

Learn how to view the summary of a Prediction Map in DORA.

[DORA 1.0] Access a Prediction Map Summary

[DORA 1.0] Duplicate a Prediction Map

Learn how to access existing Prediction Maps in order to make edits.

[DORA 1.0] Edit an Existing Prediction Map

Learn about the default parameters in DORA.

[DORA 1.0] Default Parameters

Learn about pixels presets for an Area of Interest.

[DORA 1.0] AOI Pixels Guide

DORA 1.0

Expand your dataset using DORA's Data Augmentation Modules

Data Augmentation

An overview of Data Augmentation, its role in preparing data, and its main goals in generating DORA Prediction Maps and VPS Results.

Data Augmentation: Overview

Insight into how Data Augmentation outputs are validated.

How Data Augmentation is Validated

An overview of each Data Augmentation Module output.

Raster Outputs of Data Augmentation  Models

Overview of the Data Augmentation workflow.

How Data Augmentation Works

Data Augmentation Modules Organized By Data Type

Getting Started With Data Augmentation

Overview of the Distance Maps Module for Data Augmentation

Distance Maps: Overview

Overview of the Data Density Maps Module for Data Augmentation

Data Density Maps: Overview

Overview of the Lineament Maps Module for Data Augmentation

Lineament Maps: Overview

Overview of the Computer Vision Maps Module for Data Augmentation.

Computer Vision Maps: Overview

Overview of the Texture Filter Maps Module for Data Augmentation.

Texture Filter Maps: Overview

Overview of the Multivariate Anomaly Maps Module for Data Augmentation

Multivariate Anomaly Maps: Overview

Overview of the Gridded Maps Module for Data Augmentation

Gridded Maps: Overview

Overview of the Structural Domains Maps Module for Data Augmentation.

Structural Domain Maps: Overview

Learn how to use the Texture Filter Maps Data Augmentation Module in this step-by-step walkthrough.

How to Use the Texture Filter Maps Module

Learn how to use the Computer Vision Maps Data Augmentation Module in this step-by-step walkthrough.

How to Use the Computer Vision Maps Module

Module overviews and detailed walkthroughs

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