Use of Noise to Augment Training Data: A Neural Network Method of Mineral-Potential Mapping in Regions of Limited Known Deposit Examples.
One of the main factors that affects the performance ofMLPneural networks trained using the backpropagation algorithm in mineral-potential mapping is the paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the...[Show more]
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