Methods for analysis in digital images of sedimentary rocks
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This dissertation focuses on the use of digital images to analyze sedimentary rocks. While digital images can provide important insight into a rock, the analysis of such images can be extremely time consuming. To address this problem, I explore various methods for the automation of this process. The utility of these segmented images are then demonstrated across different disciplines in geoscience. In the first study, I explore the use of traditional image processing methods to analyze a thin section for sedimentology purposes. A marker-based watershed approach incorporating both a distance and gradient map is used to identify individual grains. The algorithm is then used to measure the spatial distribution of grain size within a Permian Basin reservoir. The second study utilizes a neural network for identifying six different mineral types in a scanning electron microscope image of a shale. After assigning physical properties to each pixel, a digital rock physics experiment using finite elements is used to simulate a laboratory experiment for the estimation of elastic properties. The final study focuses on the automation of point counting in a thin section for petrography. A convolutional neural network utilizing both plane- and cross-polarized light images is used to identify the percentages of each grain type across an image. The results are comparable with a manual point count. As the amount of data collected becomes increasingly large, the automation of image analysis using the methods proposed here will allow for future users to feasibly work with such data sets.