Applications open for IndiaAI Hackathon on Mineral Targeting (India)

Aims

• The initiative aims to enable the identification of new potential areas for exploration of critical minerals like REE, Ni-PGE, and copper, as well as other commodities like diamond, iron, manganese, and gold within a pre-defined 39,000 sq. km area in the states of Karnataka and Andhra Pradesh, India.

Themes

• Identification of new potential areas for exploration of critical minerals like REE, Ni-PGE, and copper, as well as other commodities like diamond, iron, manganese, and gold within a pre-defined 39,000 sq. km area in the states of Karnataka and Andhra Pradesh, India.
• Emphasis on locating concealed & deep-seated mineralised bodies with depth modelling.
• Developing AI/ML algorithms for data cleaning, integration, modeling, and validation.
• Generation of mineral predictive maps showing exploration targets visualised through maps, sections, etc.

Funding Information

• First Prize: INR 10 Lakhs
• Second Prize: INR 7 lakhs
• Third Prize: INR 5 lakhs
• Special Prize: INR 5 lakhs for All-Women Teams (if no women team in top 3)

Awards Information

• Opportunity to Build for the Nation: Contribute to developing innovative solutions that address critical challenges faced by the country, making a direct impact on society.
• National Recognition: Gain visibility and recognition from government officials, industry leaders, and peers for your contributions and innovative ideas.
• Exposure to Real-world Challenges: Work on pressing issues faced by the nation, providing practical experience and a deep understanding of real-world problems.
• Support for Implementation: Wining solution may get potential support in scaling and implementing the solution for GSI.
• Prizes and Incentives:
  • First Prize: INR 10 Lakhs
  • Second Prize: INR 7 lakhs
  • Third Prize: INR 5 lakhs
  • Special Prize of INR 5 lakhs for All-Women Teams (if no women team in top 3)

Expected Deliverables from Participants

• Model Code and Documentation:
  • Clear and well-documented code used to build, train, and test the model.
  • Explanation of the approach used to develop the anomaly detection model.
• Project Report (PDF):
  • Name and details of Participant/Company etc. In case of a group, the name of the Team Leader and the members are to be mentioned.
  • Resources used (Hardware, Software, Manpower, etc.)
  • Data used in details (List of Geological, Geophysical, Geochemical, Petrological, Drill, Remote Sensing, any other datasets)
  • List of derived data layer/extracted feature, if any, from primary data
  • Description and significance of those derivative layers or extracted features vis-àvis mineral targeting
  • Methodology in detail, elaborating on the methodology of data processing, curation and application of techniques, classical as well as emerging methodologies, transformation operations, statistical analysis, flow chart of steps, etc.
  • Supportive documents and information related to the degree of confidence, relative contribution of input layers in the final output, etc.
  • Conceptual genetic model of the targeted mineral systems and the targeting criteria used.
• Outcome/Result as Predictive Maps, 3D models:
  • Predictive maps providing areas for mineral targeting as obtained through analysis of multi-theme data
  • 3D models/depth models of target mineralized body
• Virtual Presentation:
  • A summary of the approach, the methodology behind the anomaly detection process, findings, and recommendations.
  • Visual aids (graphs, charts, 2D maps, 3D models) to support the presentation.

Eligibility Criteria

• Company: The team may be from any registered company.
• Start-up: Alternatively, the team can qualify as a start-up company.
• Academic/R&D Organizations: Any.
• Autonomous Bodies: Autonomous bodies, including public sector organizations, are eligible to participate.
• Others: Students or researchers associated with educational institutions, or working professionals can participate in their individual capacity or as teams.

For more information, visit IndiaAI.