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We MINERIA are a company that supports the evaluation of metal mineral
resources. We handle the products of Maptek Pty in Australia. We also have
a Vulcan Geostatmodeller, which we use to evaluate metal resource deposits.
The success or failure of metal deposit development is determined by the quality of the primary data obtained up to the pre-feasibility study using boreholes and test pits, and the accuracy of the geological and ore deposit models based on the secondary data processed from these data. am. A model corresponds to a forecast weather map used for weather forecasting. Models often differ from reality, just as weather forecasts sometimes deviate, and it is important to keep the differences small.
Since the mine design is based on the model, there is a large difference between the model and reality.In other words, if the accuracy of the model is low, a redesign will be required, which often leads to project delays and increased development costs. increase. Projects that start with poor primary data are likely to fail.
In order to accurately understand the amount of resources and minable ore reserves of metal deposits, basic data of geology and deposits (topography, geological structure, faults, folds, groundwater, surface water) and geological information (rock classification, mineral symbiosis, alteration) are required. ・It is important to allocate, organize, and integrate the quality on the model. Repeated allocation and examination in comparison with basic data, geological information data, and models will increase the accuracy of the amount of resources.
Even in today's world, where AI prediction is widely used and model creation has become faster, in addition to the accuracy of the primary data mentioned above, it is necessary to take an attitude that does not accept the results of AI processing without question. Since AI processing makes predictions based on past data, it may be difficult to predict exceptions and rare events such as discontinuities. Therefore, in the modeling and evaluation of metal deposits, we need a system that can be processed using AI, but also manually adjusted and operated to gain insight. Traditionally used geostatistics is similarly not versatile in predicting ore deposit structures during model formation. There are many realities that defy statistical predictions.
To reiterate, the basis of everything is to create a geological deposit model based on appropriate primary data, evaluate it based on it, and revise it at an appropriate time.
At our company, we emphasize the quality of primary data before model formation, and conduct project evaluation using Vulcan.