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To meet the world's ever growing energy demand, the E&P industry is developing new technologies to find and drill for hydrocarbons in more challenging environments such as deep water or deep targets, and to enter areas of the world that had previously been closed to exploration activities, such as the Arctic. While the Arctic was looked at geologically several times in the last century, the technological challenges that it presents restricted activities to the periphery of this vast collection of basins. Despite the significant resource estimates shown in Figure 1, the extreme environmental conditions combined with the vast remoteness of this region have limited exploration and development efforts to date.

Feature selection is problematic when the number of potential features is very large. Absent distribution knowledge, to select a best feature set of a certain size requires that all feature sets of that size be examined. This paper considers the question in the context of variable selection for prediction based on the coefficient of determination (CoD). The CoD varies between 0 and 1, and measures the degree to which prediction is improved by using the features relative to prediction in the absence of the features. It examines the following heuristic: if we wish to find feature sets of size m with CoD exceeding δ, what is the effect of only considering a feature set if it contains a subset with CoD exceeding λ δ | max{θ1,θ2,...,θv} < λ), where θ is the CoD of the feature set and θ1,θ2,...,θv are the CoDs of the subsets. Such probabilities allow a rigorous analysis of the following decision procedure: the feature set is examined if max{θ1,θ2,...,θv} ≥ λ. Computational saving increases as λ increases, but the probability of missing desirable feature sets increases as the increment δ - λ decreases; conversely, computational saving goes down as λ decreases, but the probability of missing desirable feature sets decreases as δ - λ increases. The paper considers various loss measures pertaining to omitting feature sets based on the criteria. After specializing the matter to binary features, it considers a simulation model, and then applies the theory in the context of microarray-based genomic CoD analysis. It also provides optimal computational algorithms.

GLEAMS is a robust, stable and accurate image capture and quantification method for microarrays. It is capable of fully and automatically detecting and quantifying the expression spots. This can be done in a batch mode, without human intervention, achieving a high throughput of parallel data processing. Simple to use visual tools are provided to estimate parameters and to submit, monitor and control jobs execution. The un-supervised batch auto-alignment is based on a novel method requiring only knowledge of the number of rows and columns of dots in the array. Distances between dots along rows and columns are estimated from the image's auto- correlation function. This is also used to align the array and the sides of the image. Applying intensity and geometric constraints to the cross-correlation function between the image and a template sub-array, the location of the sub-arrays can be determined. Carefully implemented, the algorithm can approach human vision in its sensitivity and accuracy in finding the general positions of dots in a micro-array image. Subsequent spot quantification uses Ostu's thresholding method followed by some morphological operations, including the application of a constraining shape mask. Segmentation techniques are applied to detect and remove speckles from the targets and to ensure the veracity of the data extracted.