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Honors Thesis Archive

Author Jordan Hildebrandt
Title Calibrating M-Sequence and C-Sequence GPTSs with uncertainty quantification and cyclostratigraphy
Department Geology & Computer Science
Advisor John Ritter
Year 2012
Honors University Honors
Full Text View Thesis (2503 KB) Note: This is a very large file; it may be easier to download the file to your computer and open it from there.
Abstract Geomagnetic polarity timescales (GPTSs) are critical tools for dating events in the geological record. GPTSs are derived chiefly from: first, the relative ages of "block distance models" of magnetic anomaly lineations measured on the flanks of mid-ocean ridges and second, the absolute ages of radiometric dates. The Cenozoic C-sequence (spanning from the present to 80 million years ago (Ma)) GPTS is more accurate and well understood since there is more information about its constraints, but the Mesozoic M-sequence (124 Ma to 158 Ma) GPTS is not well-defined. This is primarily because data for this period is very scarce. Additionally, current M-sequence GPTSs are based on an unrealistic constant-spreading-rate assumption for a single block model, do not synthesize all available block models, do not incorporate cyclostratigraphic duration constraints, and lack stringent uncertainty tabulations. The purpose of this research is to correct these inadequacies by limiting the variation in spreading rates for all magnetic anomaly block models, not merely one or a few. Doing so will result in an improved GPTS since there is no reason for spreading zones overall to exhibit widely diverse behavior, as current assumptions imply. The method used to produce a better GPTS is the Metropolis-Hastings stochastic sampling algorithm, which permits using all available block models, radiometric ages, and cyclostratigraphy duration constraints. The resulting timescale from the method reduced the global variance in spreading rates compared to published M-sequence timescales. The algorithm was then successfully validated by using selecting only a few of the available C-sequence parameters, simulating a dearth of data, to generate a timescale that reasonably matches the accepted, more rigorously constrained C-sequence GPTS. The algorithm provides exceptional flexibility in updating the timescale for any new data collected.

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