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The development of affordable digital technologies that allow the collection and analysis of georeferenced field data represents one of the most significant changes in field-based geoscientific study since the invention of the geological map. Digital methods make it easier to re-use pre-existing data (e.g. previous field data, geophysical survey, satellite images) during renewed phases of fieldwork. Increased spatial accuracy from satellite and laser positioning systems provides access to geostatistical and geospatial analyses that can inform hypothesis testing during fieldwork. High-resolution geomatic surveys, including laser scanning methods, allow 3D photorealistic outcrop images to be captured and interpreted using novel visualization and analysis methods. In addition, better data management on projects is possible using geospatially referenced databases that match agreed international data standards. Collectively, the new techniques allow 3D models of geological architectures to be constructed directly from field data in ways that are more robust compared with the abstract models constructed traditionally by geoscientists. This development will permit explicit information on uncertainty to be carried forward from field data to the final product. Current work is focused upon the development and implementation of a more streamlined digital workflow from the initial data acquisition stage to the final project output.

New Haven, Connecticut Northeastern Forest Experiment StationThis paper describes several algorithms for computing the residual sums of squares for all possible regressions with what appears to be a minimum of arithmetic (less than six floating-point operations per regression) and shows how two of these algorithms can be combined to form a simple leap and bound technique for finding the best subsets without examining all possible subsets. The result is a reduction of several orders of magnitude in the number of operations required to find the best subsets.