Abstract

Logging of exploration drill holes is a routine practice, and its accuracy is essential for resource evaluation and planning in the minerals industry. Logged compositions record a set of material types with standardized mineralogy and texture characteristics. The material types logged may vary due to diversity in mineralization and geology, but also due to subjective biases and human error, leading to significant challenges for the industry. Thus, there is a need to validate the field logging whereby the material types and their percentages are adjusted to reconcile with laboratory assay values while retaining the physical characteristics and geologic context.

We introduce the auto-validation assistant (AVA) algorithm, which applies data mining methods to geologists’ validation patterns recorded in a training process over hundreds of intervals of iron ore exploration drill holes. The AVA modifies the material types selected in the logged composition and their percentages according to geologic rules learned in the training process and proposes to the geologist a number of validated compositions with optimized geochemistry and mineralogical hardness, while also considering visible properties such as chip shape and color. Using the confidence value provided with each validated composition, the geologist can make informed validation decisions and remains in control of the validation process, while harnessing computational power.

Experiments were conducted to evaluate the autovalidated compositions generated by AVA: one to analyze the acceptance rate of the AVA-generated compositions by geologists for 1,996 intervals in drill holes from different sites, and the other to compare manual and AVA-validated compositions using 14,600 drill holes from one entire deposit. The results showed the acceptance rate of AVA-validated compositions (without further change) of 74.3%, leading to significant time savings over tedious manual validation, while demonstrating that AVA provides comparable but more consistent results. The algorithm is fast and repeatable and can be adapted to different material types and training datasets, with potential applications beyond iron ore exploration.

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