Source of Publication
Procedia Computer Science
© 2016 The Authors. In this contribution we combine different image processing and pattern recognition methodologies to map the probability of discovering epithermal mineral deposits in the northern part of the Coromandel peninsula, in New Zealand. The objective of this work is to propose a case-study where the substitution of structural geology GIS themes (commonly developed by humans) with products derived by image processing, computer-based, semi-automatic edge detection analyses, is carried out to reduce subjective input in the prospectivity analysis. Semi-automated lineament extraction results introduced in the mineral favourability statistical modelling can more easily reveal unexpected potentially mineralised target domains, being less subjective. We present initial results of this analysis and explain some of the methodologies adopted. Preliminary results suggest that this approach increases significantly the number of geological discontinuities mapped in the region, with the following implications: (1) prospectivity models are more risk-tolerant and result in an increased number of targets; (2) increments in posterior probability affect the statistical validity of the model due to conditional independence violation, requiring careful assessment of probability overestimation; (3) the feature extraction process identifies numerous lineaments that in some instances represent false positives (lineaments determined by a variety of causes, without geological significance); however, we find that Contrast calculations in the Bayesian analysis tend to penalize these evidential themes, because of the higher number of pixels (cells) containing a positive pattern (lineament existence = 1, being positive). This aspect reduces the overall impact of these predictors on the analysis, mitigating the effect of false positives (lower positive weights of evidence). Despite the limitations, results obtained are encouraging with a clearly superior and more detailed mapping of potential structural sites and their relative probabilities of hosting epithermal deposits.
Computer Sciences | Physical Sciences and Mathematics
Bayesian Learning, Cluster Analysis, Epithermal Gold, Mineral Prospectivity, Weights of Evidence
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Feltrin, Leonardo; Motta, João Gabriel; Al-Obeidat, Feras; Marir, Farhi; and Bertelli, Martina, "Combining Weights of Evidence Analysis with Feature Extraction - A Case Study from the Hauraki Goldfield, New Zealand" (2016). All Works. 972.
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Gold: This publication is openly available in an open access journal/series