Graduate Thesis Or Dissertation

 

Creating a Predictive Model of Cinder Cone Evolution in Central Oregon Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/2b88qj581

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  • Cinder cones are useful geomorphic features for geological analysis because they generally have known initial states and follow a similar pattern of degradation as they are exposed to erosive processes. This is largely because cinder cones are produced by monogenetic eruptions. Characterizing large cinder cone fields in terms of age and composition requires significant resources, so this study aims to understand the parameters that influence cinder cone evolution in order to create a predictive age model of cones in Central Oregon. We utilize newly available high-resolution topography and new estimates of cinder cone age to evaluate a quantitative model of cone ages. The field site allows assessment of the effects of cone composition and precipitation in governing the morphology of Central Oregon cinder cones through time. We anticipate that these results will allow estimation of the age of individual cones which have not yet been dated directly. Statistical analyses show that younger cones have both significantly steeper slopes and more defined cone craters than older ones. Results also show that increased local precipitation does not appear to significantly influence cone morphology. However, composition as determined by silica content does influence initial morphology, as weight percentages vary by 15% and lead to differences of 8° in cone slope. After cone formation though, erosive processes such as mass wasting are more influential in influencing cones than chemical content. The statistical model of cone age that we create can be used to estimate cinder cone ages throughout the Central Oregon region and can be used in time-volume studies of monogenetic volcanism. The model and procedure should also be applicable in other portions of the Cascades and potentially to other volcanic fields.
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  • Ongoing Research
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  • 2019-06-05 to 2020-07-06

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