Potential of ML in Exploration

Introduction: 300 words

  1. The market
  • how much money is it spent in the exploration? (in labour capital, land, machines)
  • What is the % of exploration failure for underground and open pit
  • which is the resource with higher cost of exploration? (it should be gold mines, therefore ML would be much more useful)
  • What’s the average cost per each stage of mining? (exploration-resource development-mining-processing)

Main Body: 2400 words

  1. The standard exploration method: the geologists job
  • Procedure/method
  • Risk+time
  • % of failure/ accuracy
  • Limit of data that they can process
  • What is the most difficult mineral resource to find and why
  • Cost of exploration
  • What kind of technology it is used at the moment
  1. AI and ML:
  • General info: what they are, differences + examples of contemporary applications in other sectors
  • Specific info: how ML is applied to exploration and comparison to the standard geologist method in term of:

–  efficiency and effectiveness

  • costs vs results
  • What is their break-even point (result/cost): when it will worth use ML and why
  1. How can AI (Artificial Intelligence) and ML (Machine Learning) assist in mining exploration in order to be:
  • more effective (1-10-100 rule the cost of human data error)
  • more efficient

More effective: company examples

More efficient: company examplesConclusion: 300 words

  1. Pros and cons AI+ML
  2. Is it the cost the only restrain to their usage?
  3. Final recomandation: usage of ML when it is more effective and efficient, based on the analysis above