Identification of lithofacies using Kohonen self-organizing maps
Computers & Geosciences
Introduction: neural networks for analysis of complex scientific data: astronomy and geosciences
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
TEXTNN-A MATLAB program for textural classification using neural networks
Computers & Geosciences
Cubic-splines neural network- based system for image retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Using more than 1000 thin section photos of ancient (Phanerozoic) carbonates from different marine environments (pelagic to shallow-water) a new numerical methodology, based on digitized images of thin sections, is proposed here. In accordance with the Dunham classification, it allows the user to automatically identify carbonate textures unaffected by post-depositional modifications (recrystallization, dolomitization, meteoric dissolution and so on). The methodology uses, as input, 256 grey-tone digital image and by image processing gives, as output, a set of 23 values of numerical features measured on the whole image including the ''white areas'' (calcite cement). A multi-layer perceptron neural network takes as input this features and gives, as output, the estimated class. We used 532 images of thin sections to train the neural network, whereas to test the methodology we used 268 images taken from the same photo collection and 215 images from San Lorenzello carbonate sequence (Matese Mountains, southern Italy), Early Cretaceous in age. This technique has shown 93.3% and 93.5% of accuracy to classify automatically textures of carbonate rocks using digitized images on the 268 and 215 test sets, respectively. Therefore, the proposed methodology is a further promising application to the geosciences allowing carbonate textures of many thin sections to be identified in a rapid and accurate way. A MATLAB-based computer code has been developed for the processing and display of images.