The classification of mineral resources is the classification of mineral deposits based on their geological certainty and economic value.

A diagram like McKelvey's shows the relationship between the classifications of mineral resources, their economic value, and their geological certainty.

Mineral deposits can be classified as:

        • Ore occurrences or prospects of geological interest, but not necessarily of economic interest.
        • Mineral resources that are potentially valuable, and for which there are reasonable prospects for eventual economic extraction.
        • Ore reserves or Mena´s Ore Reserves that are valuable and that are legally, economically and technically feasible to extract.

In common with mining terminology, an ore deposit by definition must have an 'ore reserve', and may or may not have additional 'resources'.

Regarding classification, because it is an economic function, it is controlled by statutes, regulations and standards of best practices in the industry. There are several classification schemes worldwide, the Canadian CIM classification (NI 43-101), the Australasian Joint Ore Reserves Committee Code (JORC) and the South African Code for Ore Reserves. The Reporting of Mineral Resources and Mineral Reserves (SAMREC) are the general standards. Currently, the Chilean Mining Commission through international agreements has the power to authorize legally registered and registered “Competent Persons” to sign documents such as those mentioned above.

Services: Geological Models - Estimation of mineral resources and reserves

  • Integration of topographic information to the national network
  • Information unification and advanced database management
  • Review of Qa / Qc procedures. Survey of Findings and Opportunities
  • Exploratory Analysis of Univariate and Multivariate Information.
  • Data Science for Big Data jobs
  • Geological structural modeling and different types of Domains
  • GeoMetallurgical Models focused on Recovery calculations - R. Masica - F. modifiers
  • Calculation of Cut Off and / or NSR. Machine learning analysis for geology-Geometallurgy and Geoest.
  • Use of one-of-a-kind Daqras Software for BigData-Data Science analysis
  • Development of Jorc Type Documents - NI 43-101 - SK1300 among others.
  • Preparation and development of chapters in this area for the Project or LOM level.
  • Estimation of Resources and Reserves using Geostatistical techniques and others such as IVOR
  • Declaration of Resources and Reserves through QPs 
  • Expert Audits and Reviews of Resources and Reserves by Specialty QPs


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