A multilayer feed-forward neural network, trained with a gradient descent, back-propagation algorithm, is used to estimate the favourability for gold deposits using a raster GIS database for the Tenterfield 1:100 000 sheet area, New South Wales. The database consists of solid geology, regional faults, airborne magnetic and gamma-ray survey data (U, Th, K and total count channels), and 63 deposit and occurrence locations. Input to the neural network consists of feature vectors formed by combining the values from co-registered grid cells in each GIS thematic layer. The network was trained using binary target values to indicate the presence or absence of deposits. Although the neural network was trained as a binary classifier, output values for the trained network are in the range [0.1, 0.9] and are interpreted to indicate the degree of similarity of each input vector to a composite of all the deposit vectors used in training. These values are rescaled to produce a multi-class prospectivity map. To validate and assess the effectiveness of the neural-network method, mineral-prospectivity maps are also prepared using the empirical weights of evidence and the conceptual fuzzy-logic methods. The neural-network method produces a geologically plausible mineral-prospectivity map similar, but superior, to the fuzzy logic and weights of evidence maps. The results of this study indicate that the use of neural networks for the integration of large multisource datasets used in regional mineral exploration, and for prediction of mineral prospectivity, offers several advantages over existing methods. These include the ability of neural networks to: (i) respond to critical combinations of parameters rather than increase the estimated prospectivity in response to each individual favourable parameter: (ii) combine datasets without the loss of information inherent in existing methods: and (iii) produce results that are relatively unaffected by redundant data, spurious data and data containing multiple populations. Statistical measures of map quality indicate that the neural-network method performs as well as, or better than, existing methods while using approximately one-third less data than the weights of evidence method.
Brown, W. M., Gedeon, T. D., Groves, D., & Barnes, R. G. (2000). Artificial neural networks: a new method for mineral prospectivity mapping. Australian Journal of Earth Sciences, 47, 757-770. https://doi.org/10.1046/j.1440-0952.2000.00807.x