Worker Safety Monitoring via Wearable Sensors in the Mining Industry

ADDA-Mining AI

Enhancing Occupational Health and Hazard Prevention Through IoT and AI

Executive Summary

  • Traditional mineral exploration is costly, time-consuming, and high-risk, with only 1 in 1,000 prospects becoming a mine (SRK Consulting).
  • AI-powered geospatial analysis accelerates discovery by processing satellite imagery, geological surveys, and geochemical data with machine learning.
  • Key benefits:
    • 50-70% faster target identification (Goldspot Discoveries).
    • 30% reduction in exploration costs (Deloitte).
    • Higher success rates through predictive mineral modeling.
  • Market impact: AI in mining exploration expected to grow at 23.4% CAGR (2023-2030, Grand View Research).

Key Challenges in Mineral Exploration

  • High Failure Rates – Most exploration projects do not yield economically viable deposits.
  • Data Overload – Terabytes of geospatial data from satellites, drones, and ground surveys.
  • Manual Interpretation Bias – Geologists’ subjective analysis can miss subtle patterns.
  • Regulatory & Environmental Hurdles – Permitting delays increase project timelines.
  • Limited Access to Remote Areas – AI can analyze regions before physical deployment.

Solution: AI-Driven Geospatial Mineral Discovery

  1. Multispectral & Hyperspectral Satellite Imagery
  • NASA Landsat, Sentinel-2, and private satellites detect mineral signatures.
  • AI classifies lithology, alteration zones, and ore indicators (e.g., iron oxides, clays).
  1. Machine Learning for Predictive Targeting
  • Neural networks trained on historical discovery data predict high-potential zones.
  • Unsupervised learning identifies anomalies in geochemical datasets.
  1. Drone-Based LiDAR & Geophysical Surveys
  • High-resolution 3D mapping of terrain and subsurface structures.
  • AI merges data from magnetics, gravity, and EM surveys.
  1. Generative AI for Prospect Generation
  • Simulates hypothetical deposit models based on geological rules.
  1. Blockchain for Data Integrity
  • Secure, tamper-proof exploration data logs for investors and regulators.

Outcomes & Benefits

✔ Faster Discovery Timelines – AI narrows targets from years to months.
✔ Lower Exploration Costs – Reduced drilling waste and field labor.
✔ Higher Accuracy – AI detects subtle patterns missed by humans.
✔ Reduced Environmental Impact – Fewer unnecessary drill sites.
✔ Attracts Investment – Data-driven insights improve project funding.

Future Technology Trends

  • Quantum Machine Learning – Processes geospatial data 1,000x faster.
  • Swarm Robotics – Autonomous drones collect and analyze field data in real time.
  • AI-Enhanced Core Logging – Instant mineral identification from drill samples.
  • Metaverse for Exploration – Virtual reality geoscience collaboration.

 

Insights from Industry Leaders

  • KoBold Metals (backed by Bill Gates) uses AI to discover copper/cobalt deposits in Zambia.
  • Rio Tinto’s MineLab analyzes 3D geological models with AI.
  • Goldspot Discoveries reduced exploration risk by 80% for Eric Sprott’s mining ventures.

Roadmap for Implementation

Phase

Key Actions

1. Data Aggregation

Collect satellite, geophysical, and legacy exploration data.

2. AI Model Training

Develop custom algorithms for target mineralization.

3. Field Validation

Ground-truth AI targets with selective drilling.

4. Scalable Deployment

Expand AI to regional/global exploration portfolios.

Conclusion

AI-geospatial analysis is transforming mineral exploration, turning guessing games into data-driven science. Early adopters gain first-mover advantage in discovering the next generation of critical mineral deposits.

Next Steps:

  • Audit existing exploration data for AI readiness.
  • Partner with AI-geospatial firms (e.g., KoBold, Earth AI).
  • Train geologists in AI-assisted interpretation.

Contact Us:
✉ hi@adda.co.id | 🌐 www.adda.co.id