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This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models. / Thesis (Master, Mining Engineering) -- Queen's University, 2007-11-28 15:22:17.454 / I would like to thank the financial support provided by the George C. Bateman and J. J. Denny Graduate Fellowship, as well as funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) provided via NSERC grant support to Dr. Daneshmend.

This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-04-23 11:59:07.806

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The first eight chapters of this thesis describe a study of the magnetic effects of drilling on bore cores of sedimentary rocks. Extensive rock and palaeo- magnetic methods were used to investigate such effects in three collections of bore cores from the North Sea and Sellafield, U.K., and Prudhoe Bay, Alaska. It is evident that a drilling imposed remanent magnetisation (DIRM) resides in the North Sea and Prudhoe Bay bore cores which is characterised by symmetries in its intensity and direction relative to the core axis. Such DIRM correlated well with the theoretically modelled magnetic field at one end of a steel drill barrel. The DIRM intensity distribution also appeared to be correlated with variation in the radial remanence susceptibility (i.e. the capacity of remanence acquisition) in the North Sea and Prudhoe Bay cores and magnetic susceptibility in the North Sea cores. Simulation experiments of shock impact conducted on bore core materials suggests that shock/vibration of the drill barrel is the major process that is responsible for the radial variation in core magnetic properties. Titanomagnetite (including magnetite) and pyrrhotite are the major carriers of DIRM but there is no DIRM identified in bore cores in which hematite is the only ferromagnetic mineral. Chapter 9 describes a novel attempt in using fractal geometry to statistically depict the geomagnetic field reversal sequence. A fractal distribution is shown to occur for longer geomagnetic polarity intervals (> 0.28 Ma) in terms of a power law relationship between interval length and cumulative number for the last 158 Ma. A simulation study indicates that the deviation from the power law at shorter intervals (< 0.28 Ma) is caused by missing of short intervals due to the limit of resolving power. This is strongly supported by a fractal model (i.e. a Cantor set) introduced for relating the shortest polarity interval, the transition time and the fractal dimension. Normal and reversed polarity intervals have similar fractal dimensions, suggesting that there is no, statistically, fundamental difference between the two magnetic polarity states.

Long years of experience in the field and sometimes in the lab are required to develop consultants. Texas A&M University recently has established a new method to develop a drilling expert system that can be used as a training tool for young engineers or as a consultation system in various drilling engineering concepts such as drilling fluids, cementing, completion, well control, and underbalanced drilling practices. This method is done by proposing a set of guidelines for the optimal drilling operations in different focus areas, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Optimum practices collected from literature review and experts' opinions, are integrated into a Bayesian Network BN to simulate likely scenarios of its use that will honor efficient practices when dictated by varying certain parameters. The advantage of the Artificial Bayesian Intelligence method is that it can be updated easily when dealing with different opinions. To the best of our knowledge, this study is the first to show a flexible systematic method to design drilling expert systems. We used these best practices to build decision trees that allow the user to take an elementary data set and end up with a decision that honors the best practices.

Thesis (Ph.D.)--University of Tulsa, 1994. / Includes bibliographical references (leaves 110-112).

Thesis (B.S.)--University of Missouri, School of Mines and Metallurgy, 1914. / The entire thesis text is included in file. Typescript. Illustrated by author. Title from title screen of thesis/dissertation PDF file (viewed March 31, 2009)