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    Optimal Thresholding of Predictors in Mineral Prospectivity Analysis

    82000.pdf (1.952Mb)
    Access Status
    Open access
    Authors
    Baddeley, Adrian
    Brown, Warick
    Milne, Robin K
    Nair, Gopalan
    Rakshit, Suman
    Lawrence, Tom
    Phatak, Aloke
    Fu, Shih Ching
    Date
    2020
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Baddeley, A. and Brown, W. and Milne, R.K. and Nair, G. and Rakshit, S. and Lawrence, T. and Phatak, A. et al. 2020. Optimal Thresholding of Predictors in Mineral Prospectivity Analysis. Natural Resources Research.
    Source Title
    Natural Resources Research
    DOI
    10.1007/s11053-020-09769-2
    ISSN
    1520-7439
    Faculty
    Faculty of Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/IC180100030
    Remarks

    This is a post-peer-review, pre-copyedit version of an article published in Natural Resources Research. The final authenticated version is available online at: http://doi.org/10.1007/s11053-020-09769-2

    URI
    http://hdl.handle.net/20.500.11937/81939
    Collection
    • Curtin Research Publications
    Abstract

    © 2020, International Association for Mathematical Geosciences.

    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.

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