Destroying the Distinction Between Explicit and Implicit Geological Modelling

Danny Kentwell
Monday, November 25, 2019
First presented: 
Mining Geology 2019
Published paper

Mathematically and topologically there is a clear distinction between the terms explicit and implicit surface modelling. In the mining and financial industries, however, these terms have come to represent something else. To some people, they represent a divide, a division, a hard boundary (to use a geostatistical term) that is considered the border between good and evil. The misconception that a geological model made with the assistance of a so-called implicit modelling tool is either better or worse than a model made with a so-called traditional or explicit tool or methodology needs to be destroyed.
The same divide existed during the change over from sectional pencil and paper modelling to sectional computer-based modelling and that was overcome (eventually). The same divide also existed (and still exists in some places) around the use of ordinary kriging instead of inverse distance for block estimation.
Implicit models require significant amounts of “manual” input before they are fit for use and  the same levels of geological, volumetric and statistical validation. They also require peer review. The primary advantage of implicit modelling methods is speed, not accuracy, not unbiasedness, not detail, but speed. Speed leads to the ability to test multiple scenarios and models and allows for modelling by trial and error. Think – model – examine – accept / reject – modify – repeat until happy.
This presentation examines some history of methods of geological modelling, the origin of the term “implicit” in this context and proposes that there is not much that is implicit about “implicit” modelling.

Feature Author

Danny Kentwell

Danny Kentwell is a geostatistician with a background in geological modelling, mine planning and surveying. He has 25 years’ international experience with varied commodities including gold, copper, mineral sands, iron ore, nickel laterites, nickel sulphides and phosphate. Danny’s skills cover, 3D modelling, Resource estimation, open pit optimisation scheduling and design. His geostatistical expertise includes standard and recoverable resource estimation techniques such as uniform conditioning, indicator kriging and conditional simulation as well as multivariate estimation and simulation. As a geostatistician and engineer, he has an excellent understanding of the advantages and limitations of different resource estimation techniques, their resulting block grade, tonnage and value curves and their use in mine planning. Danny also has experience in applying geostatistical techniques to waste characterisation and determination of sampling adequacy from very small data sets.

Principal Consultant (Geostatistics)
MSc (Mathematics & Planning; Geostatistics), FAusIMM
SRK Melbourne
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