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This paper describes a computer vision approach to automated rapid-throughput taxonomic identification of stonefly larvae. The long-term goal of this research is to develop a cost-effective method for environmental monitoring based on automated identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) identification of regions of interest, (b) representation of those regions as SIFT vectors [1], (c) classification of the SIFT vectors into learned "features" to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step.Send offprint requests to: Tom Dietterich, 1145 Kelley Engineering Center, Corvallis, OR 97331-5501, USA, tgd@eecs.oregonstate.eduWe evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria, are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doroneuria) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.

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CO2 sequestration and enhanced gas recovery (CS-EGR) is a viable option with enormous potentials to produce shale gas. However, the microscopic competitive sorption behaviors of CH4 and CO2 in various clay minerals that are an important constituent of shale at actual formation conditions are still less clear. In this work, we study CO2/CH4 binary mixture competitive sorption in various clay minerals (montmorillonite, illite, and kaolinite) by using grand canonical Monte Carlo simulations. The effects of the clay mineral types and possible stratigraphic conditions, including temperature, pressure, CO2/CH4 molar fraction, and selectivity, are discussed in detail. The results demonstrate that the CO2 sorption capacity in the clay mineral follows an order of montmorillonite > illite > kaolinite. CO2 molecules are prone to be adsorbed on the surfaces of montmorillonite and illite nanopores with cation exchange than on the surface of the kaolinite nanopore without cation exchange. Moreover, cation exchange could distinctly increase the CO2/CH4 adsorption ratio so that the first layer of CH4 molecules can be displaced by CO2 molecules. The replacement ratio of CH4 is related to the type of adsorbent, which is independent of the original formation pressure. In addition, a case study is designed to quantify the enhanced gas recovery (EGR) and CO2–CH4 displacement efficiency. With a higher reservoir initial pressure when injecting CO2, the EGR of adsorbed CH4 gas could increase up to 28.97%. Our findings provide insights into gas mixture sorption in shale reservoirs and provide important guidelines for CS-EGR projects.

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