GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 286-7
Presentation Time: 3:30 PM


TRAMPUSH, Sheila, Department of Geography, University of California, Berkeley, 507 McCone Hall, Berkeley, CA 94720 and HAJEK, Elizabeth, Department of Geosciences, Penn State University, 511 Deike Building, University Park, PA 16802

Stratigraphy is the result of complex interactions between surface processes that operate on a wide range of spatial and temporal scales. Temporally, stratigraphy records everything from events that occurred over a matter of hours (e.g. flood deposits) to millennial patterns of sediment supply and accommodation (e.g. basin filling patterns). Spatially, the signal of these processes can be spread over centimeters (e.g. ripples), meters (e.g. channel-belt deposits), or kilometers (e.g. basin fills). Reconstructing the complete history of a basin is made even more difficult when there is limited ability to sample the entire deposit: frequently outcrop exposures are limited, and subsurface data are rare and expensive to obtain. Here, we use a reduced-complexity delta model (DeltaRCM) to understand how landscape dynamics can be leveraged to design effective and parsimonious sampling plan for reconstructing paleoenvironmental conditions in fluvial-deltaic deposits. We use a variety of statistical techniques to calculate the minimum number and spacing of samples needed to reconstruct an unbiased record of events of various durations. We find that the minimum number of samples needed depends strongly on the duration of the record relative to both the timescale of the longest timescale surface process and the accumulation rate over the interval of interest. The minimum spacing of the samples depends strongly on the subsurface heterogeneity which is a function of both long and short timescale processes. In general, highly cohesive deltas are more spatially heterogeneous and thus can require smaller minimum spacing. In designing effective sampling plans for basins, we recommend that simple measures of a deposit (e.g. average sand body dimensions, long term accumulation rate) can be used to assess how dense a sampling network is needed to create a complete, unbiased record of earth history.