Paper No. 9
Presentation Time: 3:15 PM

BUILDING A ROSETTA STONE FOR CYCLOSTRATIGRAPHIC ANALYSIS


PETERSON, John A., Department of Geology, University of California, Davis, One Shields Avenue, Davis, CA 95616, MONTAÑEZ, Isabel P., Department of Earth and Planetary Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616 and EROS, J. Michael, Exxon-Mobil Development Company, CORP-GP6-615, 12450 Greenspoint Dr, Houston, TX 77060, japeterson@ucdavis.edu

Cyclostratigraphic analysis continues to be used to in order to better constrain intervals of geologic time and to measure the rates of processes. In order to build long-term, robust timing control to sedimentary records that demonstrate potential orbital forcing it is critical to analyze in the time domain. Continuous sedimentation is rare in any location and constant sedimentation is very unlikely. Solutions generated using different stratigraphic columns under the same forcing conditions should give the same solution, but assuming constant and continuous sedimentation does not generate comparable results. Results using a Monte Carlo approach that allows sedimentation parameters to have some reasonable randomness generate spectral solutions that show the same forcing signal even when the stratigraphy is different.

To build a long-term cyclostratigraphic record, it is necessary to combine different stratigraphic columns and this method can require using multiple types of data or proxies. Longer duration records are also more likely to have hiatuses in them. In records where the stratigraphic location of hiatuses are known, finding the duration of those hiatuses is difficult. A new method is presented that uses an inverse model to solve for hiatus durations that maximizes the power of peaks in spectral analysis of the signal. This inverse method, combined with the Monte Carlo approach, generates hiatus durations in long-duration records that can provide some information on a signal which has subsequently been erased from the geologic record.

These methods are demonstrated using synthetic datasets, stratigraphic datasets from the Carboniferous record in the Donets basin, Ukraine, and remote sensing from Mars.