Long demonstrations

L1 Designing a Sedimentology Course Around Field Projects With Realistic Scenarios (Bosiljka Glumac, Smith College)
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The Smith College Sedimentology course is an example of a course structured around projects, most of which are field based. The projects are carefully designed to take advantage of the local geology and to address a variety of topics. Of utmost importance in designing individual projects is demonstrating the relevance of the work the students do. Therefore the projects are designed to mimic real-life situations: for example, the students address concerns of a local farmer, or have roles as field conference organizers and collaborators (with paleontologists) on a multidisciplinary research project.

L2 Stealing data: Deriving bedform phase diagrams (Tom Hickson, University of St. Thomas)
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"Classic" datasets can easily be "stolen" using freeware software, such that students can work with substantial, real-world data to solve "classical" problems in sedimentary geology. Bedform phase diagrams constitute a perfect example: the original data were collected through flume experiments and the phase space was delineated based on the results. Students rarely get the opportunity to understand how these diagrams were created, their strengths and weaknesses, the high degree of interpretation that lies behind them. This session is designed to work through this example and to brainstorm other classic datasets in sedimentary geology that might be similarly useful. If you have a wireless laptop, bring it to the session.

L3 Analysis of Milankovitch rhythms in ancient lake deposits (Linda Hinnov, Johns Hopkins University)

This demonstration examines connections between sedimentary geology, paleoclimatology and astronomy, through the analysis of sediments that were forced by Earth's orbital variations ("Milankovitch cycles"). A natural time series derived from an ancient lake deposit will be quantitatively analyzed to assess evidence for paleoclimate forcing by Earth's orbital parameters. Three sets of exercises will be presented that focus on: 1) basic spectral analysis; 2) the description of Earth's orbitally forced insolation; and 3) the interpretation of Milankovitch rhythms in a lake-depth proxy time series reconstructed from the Triassic Lockatong Formation, Newark Group, eastern North America. The exercises will be demonstrated using Excel and Web-accessible freeware applications that perform time series analysis and model Milankovitch cycles.

L4 Understanding bedforms and cross-stratification based on field data collection (Peter Lea, Bowdoin College)
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Field trips to modern depositional environments energize students and allow them to make real-world connections between bedforms and their formative flows. Along the Maine coast, students use a learning cycle of prediction-observation-comparison to investigate diverse bedforms exposed at low tide at a beach/inlet/tidal-delta complex. During a second trip, students investigate the hydrodynamics and bedload transport of subtidal dunes using bathymetric and ADCP data. These investigations of "bedforms in action" provide a firm foundation for interpreting flow conditions from sedimentary structures.

L5 Estimation in Sedimentary Geology: Getting Students Comfortable with Rough Calculations of Rates and Magnitudes (Chris Paola, University of Minnesota)
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Undergraduates typically learn to calculate in courses like physics, engineering, and mathematics where it is feasible and desirable to make relatively precise calculations. Important variables in physical sedimentary geology (e.g. sediment fluxes, flow depths, channel slopes, sedimentation rates, settling velocities) generally cannot be constrained to comparable levels of precision; often an order of magnitude or so is the best we can do. Because the precise quantitative tools that students have learned are often not applicable, and because our field has a history of being relatively descriptive, it's easy to skip over quantitative methods in sedimentary geology altogether, or to relegate them to traditional but restrictive application areas (e.g. statistics of size distributions).

The theme of this module is that the most important quantitative skill in sedimentary geology is not complex mathematics but rather a habit of approximate but accurate quantitative thinking. The most useful tool is the ability to make reasonable order of magnitude estimates based on a sense of the magnitudes of important quantities like settling velocity, river slope, subsidence rate, etc. We discuss practical estimation methods based on field observable quantities like grain size and flow depth, as well as methods for applying general geologic knowledge (e.g. rates of plate motion) to estimation of important quantities in sedimentary geology.