MeysamRouhnavaz presents a hands-on case study using Azure AI and Machine Learning to predict gold-rich zones from satellite data, demonstrating the full data science workflow from preprocessing to production in mineral exploration.

From Space to Subsurface: Predicting Gold Zones with Azure AI and Machine Learning

Overview

This case study details how MeysamRouhnavaz and team leveraged Microsoft Azure’s AI capabilities and machine learning services to revolutionize gold prospecting. By creating an automated pipeline that processes satellite imagery and predicts high-potential mineral zones, the project demonstrates a modern approach to geoscience and mining using Azure cloud technology.

1. Translating Satellite Imagery Into Features

  • Used Sentinel-2 satellite imagery over the area of interest (AOI).
  • Derived alteration indices relevant to mineral exploration:
    • Clay Index (hydrothermal alteration proxy)
    • Fe (Iron Oxide) Index
    • Silica Ratio
    • NDMI (Normalized Difference Moisture Index)
  • Leveraged Azure Notebooks and Python to clean/process imagery, converting raw bands into geochemical features.

2. Discovering Patterns with Azure AI

  • Employed unsupervised clustering (KMeans) using Azure Machine Learning Studio to explore geochemical feature groupings.
  • Identified meaningful clusters, notably one strongly associated with gold-rich zones.
  • Showcases AI’s strength in pattern recognition beyond human capabilities.

3. Scaling with Automated Machine Learning

  • Applied Azure AutoML to train a classification model on dense, spatial data:
    • 7,200+ datapoints at ~50m resolution
    • Covering 14 km² region
  • Utilized Azure Compute Instance and Compute Cluster for scalable, cost-effective runs with early stopping to minimize resource consumption.
  • Models were trained, validated, and registered with Azure ML and stored in Azure Storage.

4. Validation Against Field Data

  • Performed field sampling and lab assays for gold concentration at key points.
  • Validated that the model’s ‘Class 0’ cluster correlated with gold-rich samples.
  • Provided AI-augmented evidence for geologists to prioritize drilling locations.

5. Traditional vs AI-Driven Workflow

  • Traditional exploration: Time-consuming, resource intensive, based on manual surveys.
  • AI-based approach: Automated, data-driven, delivers faster, more reliable results with substantial cost and time savings.

Azure Feature Highlights

  • Azure Machine Learning Studio: AutoML, experiment tracking
  • Azure Storage: Seamless geospatial data access
  • Azure OpenAI Service: Enhanced reports and interaction
  • Azure Notebooks: Custom preprocessing and validation
  • Compute Cluster: Scalable training resources
  • Model Registry: Experiment reproducibility and deployment

Key Takeaways

  • AI transforms mineral exploration from guesswork to intelligence.
  • Workflow highlights:
    • Extracting features from satellite data
    • Unsupervised clustering
    • Automated, scalable classification
    • Spatially-complete predictions
    • Field validation of outcomes
  • The solution is scalable, reusable, and production ready, supporting global teams in geoscience and mining.

Impact

By combining Azure’s AI, ML, and cloud tools, the project created a robust pipeline for mineral exploration—reducing risk, accelerating discovery, and empowering geologists with data-driven insights before drilling begins.

For more insights or collaboration, especially in Mining, Geoscience, or AI for Earth, connect with MeysamRouhnavaz. The team aims to advance AI adoption in diverse industries via strategic partnership and innovation.

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