Quality Control in Mineral Exploration

There many goals associated with a QA/QC program for the mineral industry:

• Prevent the entry of large errors into the geological Database.

• Demonstrate that sampling and analytical variances are small relative to geological variance.

• Provide assurance that the accuracy of the data can be confirmed.

• Save management time by automating sample names and performing automatic checks on sample names.

• Allow problems to be realized rapidly to minimize their effects.

• Keep an archive for a complete audit trail.

• Comply meticulously with government regulations and standards with a minimal amount of employee time and effort.

The Second European Conference on Geostatistics for Environmental Applications took place in Valencia, November 18-20, 1998. Two years have past from the first meeting in Lisbon and the geostatistical community has kept active in the environmental field. In these days of congress inflation, we feel that continuity can only be achieved by ensuring quality in the papers. For this reason, all papers in the book have been reviewed by, at least, two referees, and care has been taken to ensure that the reviewer comments have been incorporated in the final version of the manuscript. We are thankful to the members of the scientific committee for their timely review of the scripts. All in all, there are three keynote papers from experts in soil science, climatology and ecology and 43 contributed papers providing a good indication of the status of geostatistics as applied in the environmental field all over the world. We feel now confident that the geoENV conference series, seeded around a coffee table almost six years ago, will march firmly into the next century. <...>

Ordinary kriging and non-linear geostatistical estimators are now well accepted methods in mining grade control and mine resource estimation. Kriging is also a necessary step in the most commonly used methods of conditional simulation used in the mining industry. In both kriging and conditional simulation, the search volume or ‘kriging neighbourhood’ is defined by the user. The definition of this search can have a very significant impact on the outcome of the kriging estimate or the quality of the conditioning of a simulation.

Every day, somewhere in the world, decisions are made about how public lands that might contain undiscovered resources should be used or whether to invest in exploration for minerals. Less frequently, decisions are made concerning mineral resource adequacy, national policy, and regional development. Naturally, the people making the decisions would like to know the exact consequences of the decisions before the decisions are made. Unfortunately, it is not possible to inform these decision-makers, with any certainty, about amounts, discoverability, or economics of undiscovered mineral resources.