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Title: Neural network studies of lithofacies classification
Author: Harris, David Anthony
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1994
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Exploration for hydrocarbons and other resources requires that large amounts of data be interpreted and used to infer the geology of extensive regions. Many different types of data are used. They are interpreted by geologists and sedimentologists in the light of experience. Artificial neural network models implemented on computers provide a powerful means of performing tasks such as pattern classification. Such tasks are difficult to perform using rule based methods, as we often do not know how to specify appropriate rules. We show that artificial neural networks can be used to discriminate between images of different lithofacies (types of rocks). This discrimination is based upon textural differences in the rocks, which are quantified by measures of texture derived from the rock images and used as inputs to the network. Neural network performance is good compared to a very simple alternative technique, that of K nearest neighbours. A particular set of texture measures is that based on the grey level coocurrence method. These measures have interesting properties; in particular, their expectation values can be calculated exactly for images generated by exactly solvable Ising models. The measures are themselves probabilities for the joint distribution of pixel values in an image, so that they can be used to generate images in a stochastic process.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available