Spatial mathematical models for mineral potential mapping

Porwal, Alok, Carranza, E.J.M., and Hale, M. (2011) Spatial mathematical models for mineral potential mapping. In: Ghosh, Parthasarathi, (ed.) Numerical Methods and Models in Earth Science. New India Publishing Agency, New Delhi, India, pp. 99-119.

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A key problem in spatial-mathematical-model-based mineral potential mapping is the selection of appropriate functions that can effectively approximate the complex relationship between target mineral deposits and recognition criteria. This paper evaluates a series of spatial mathematical models based on different linear and non-linear functions by applying them to base-metal potential mapping of the Aravalli province, western India. Linear models applied are an extended weights-of-evidence model and a hybrid fuzzy weights-of evidence model, while non-linear models are knowledge and data-driven fuzzy models, a neural network model, a hybrid neuro-fuzzy model and an augmented naive Bayesian classifier model. The parameters of the knowledge-driven fuzzy model are estimated from the expert knowledge, while those of the neural network and Bayesian classifier model are estimated from the data. The two hybrid models use both expert knowledge and data for parameter estimation.

As compared to the linear models, the non-linear models generally perform better in predicting the known base-metal deposits in the study area. Although the linear models do not fit the data as efficiently as the non-linear models, they are easier to implement using basic GIS functionalities and their parameters are more amenable to geoscientific interpretation. In addition, the linear models are less susceptible to the curse of dimensionality as compared to non-linear models, which makes them more suitable for applications to mineral potential mapping of the areas where there is a paucity of training mineral deposits. The hybrid models that conjunctively use both knowledge and data for parameter estimation generally perform better than purely knowledge-driven or purely data-driven models.

Item ID: 40325
Item Type: Book Chapter (Reference)
ISBN: 978-93-8023541-7
Date Deposited: 11 Mar 2016 00:31
FoR Codes: 04 EARTH SCIENCES > 0499 Other Earth Sciences > 049999 Earth Sciences not elsewhere classified @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080205 Numerical Computation @ 25%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 25%
SEO Codes: 84 MINERAL RESOURCES (excl. Energy Resources) > 8401 Mineral Exploration > 840199 Mineral Exploration not elsewhere classified @ 100%
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