B-cell receptor (BCR) signaling has been inferred as an important mechanism for disease progression in chronic lympho

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Ground-based hyperspectral imaging is used for development of digital outcrop models which can facilitate detailed qualitative and quantitative sedimentological analysis and augment the study of depositional environment, diagenetic processes, and hydrocarbon reservoir characterization in areas which are physically inaccessible. For this investigation, ground-based hyperspectral imaging is combined with terrestrial laser scanning to produce mineralogical maps of Late Albian rudist buildups of the Edwards formation in the Lake Georgetown Spillway in Williamson County, Texas. The Edwards Formation consists of shallow water deposits of reef and associated interreef facies. It is an aquifer in western Texas and was investigated as a hydrocarbon play in south Texas. Hyperspectral data were registered to a geometrically accurate laser point cloudgenerated mesh with sub-pixel accuracy and were used to map compositional variation by distinguishing spectral properties unique to each material. More calcitic flat-topped toucasid-rich bioherm facies were distinguished from overlying porous sucrosic dolostones, and peloid wackestones and packstones of back-reef facies. Ground truth was established by petrographic study of samples from this area. This research integrates high-resolution datasets to analyze geometrical and compositional properties of this carbonate formation at a finer scale than traditional methods have achieved and to model the geometry and composition of rudist buildups.

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The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived from mineral mixtures of known abundance and are used for mineral mapping. Additionally, three well-known classification techniques—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network—are compared. Point counting results from petrographic thin sections are used for validation the limestone samples, and QEMSCAN mineral maps for the sulfide samples. For classifying the carbonates, the SVM classifier produced results that are closest to the training set—with 84.4% accuracy and a kappa coefficient of 0.8. For classifying sulfides, SAM produced mineral abundances that were closest to the validation data, possibly due to the low reflectance of sulfides throughout the short-wave infrared spectrum with some differences in the overall spectral shape.