Optimal Thresholding of Predictors in Mineral Prospectivity Analysis

Adrian Baddeley, Warick Brown, Robin K. Milne, Gopalan Nair, Suman Rakshit, Tom Lawrence, Aloke Phatak, Shih Ching Fu

Research output: Contribution to journalArticlepeer-review

2 Citations (Web of Science)


Some methods for analysing mineral prospectivity, especially the weights of evidence technique, require the predictor variables to be binary values. When the original evidence data are numerical values, such as geochemical indices, they can be converted to binary values by thresholding. When the evidence layer is a spatial feature such as a geological fault system, it can be converted to a binary predictor by buffering at a suitable cut-off distance. This paper reviews methods for selecting the best threshold or cut-off value and compares their performance. The review covers techniques which are well known in prospectivity analysis as well as unfamiliar techniques borrowed from other literature. Methods include maximisation of the estimated contrast, Studentised contrast, χ2 test statistic, Youden criterion, statistical likelihood, Akman–Raftery criterion, and curvature of the capture–efficiency curve. We identify connections between the different methods, and we highlight a common technical error in their application. Simulation experiments indicate that the Youden criterion has the best performance for selection of the threshold or cut-off value, assuming that a simple binary threshold relationship truly holds. If the relationship between predictor and prospectivity is more complicated, then the likelihood method is the most easily adaptable. The weights-of-evidence contrast performs poorly overall. These conclusions are supported by our analysis of data from the Murchison goldfields, Western Australia. We also propose a bootstrap method for calculating standard errors and confidence intervals for the location of the threshold.

Original languageEnglish
Pages (from-to)923-969
Number of pages47
JournalNatural Resources Research
Issue number2
Early online date11 Nov 2020
Publication statusPublished - Apr 2021


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