The texture of digital rock images, as recorded, for instance, with borehole imaging devices, is shown to reflect different bedding types. Textural segmentation of borehole images, therefore, subdivides the recorded sequence into bedding units. We show that a textural segmentation algorithm based on the concept of texture energy achieves good results when compared with synthetic as well as real data in which petroleum geologists have performed zonations on cores. Texture energy involves filtering of the original image with a set of texture sensitive masks. The filtering is done as a finite convolution over the size of the masks. On the resulting images the variance is computed over a relatively large sliding window, which, in its practical implementation, covers the full width of the image. The resulting nine one-dimensional curves are then clustered hierarchically into a user-determined number of image texture or lithological bedding classes. Principal component analysis previous to clustering can be used to reduce redundancy in the data. A recurring and relatively ill-defined problem in this field are macro-textures, i.e., the cyclic interbedding of two or more bedding types. We show that sliding Fourier transforms and variable mask scale can successfully address the zonation of macro-textures. In general, the method gives best results with mask sizes equivalent to 2–4 centimeters, reflecting the length scale at which the investigated geological bedding seems to have its highest variation. 相似文献
The Rockhole area, Northern Territory, Australia, hosts a number of Proterozoic unconformity-related uranium deposits. The geology of the area features within Paleoproterozoic rocks of the Pine Creek Orogen, near the unconformity with overlying platform cover sandstone of the Paleo- to Mesoproterozoic McArthur Basin. Landsat Enhanced Thematic Mapper plus (ETM+) data was used in the Rockhole area to assist in mapping geological structures and lithology, and to identify anomalous concentrations of ferrous minerals, the product of alteration, which can be indicators of buried uranium mineralization. Several image-processing procedures were applied to the ETM+ data to identify, isolate and enhance mineralogical information as simple and complex false color composites. ETM+ 754 shown as red green and blue respectively was the best simple image. Overall, complex images based on Principal Component Analysis proved to be the most useful products. Sandstone, shale and siltstone, the target lithologies, Koolpin Formation, the target stratigraphic unit, and bleaching pattern due to the removal of iron(II) compounds, the target alteration pattern, were confidently mapped to provide information required by the mineral emplacement model, which ultimately identified areas of likely uranium mineralization. Thus the contrasting behavior of the two principle oxidation states of uranium and iron can be utilized to map/delineate bleached alteration zones associated with economic concentrations of uranium using multispectral sensors like Landsat or better hyperspectral sensors. 相似文献