Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia

Siddharth Hariharan, Siddhesh Tirodkar, Alok Porwal, Avik Bhattacharya, Aurore Joly

    Research output: Contribution to journalArticle

    26 Citations (Scopus)

    Abstract

    Data-driven prospectivity modelling of greenfields terrains is challenging because very few deposits are available and the training data are overwhelmingly dominated by non-deposit samples.This could lead to biased estimates of model parameters. In the present study involving Random Forest (RF)-based gold prospectivity modelling of the Tanami region, a greenfields terrain in Western Australia, we apply the Synthetic Minority Over-sampling Technique to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50. An optimal threshold range is determined objectively using statistical measures such as the data sensitivity, specificity, kappa and per cent correctly classified. The RF regression modelling with the modified dataset of close to 50:50 sample ratio of deposit to non-deposit delineates 4.67% of the study area as high prospectivity areas as compared to only 1.06% by the original dataset, implying that the original “sparse” dataset underestimates prospectivity.

    Original languageEnglish
    Pages (from-to)489–507
    Number of pages19
    JournalNatural Resources Research
    Volume26
    Issue number4
    Early online date19 Apr 2017
    DOIs
    Publication statusPublished - Oct 2017

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