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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Multi-Proxy ~8500 Year Paleoenvironmental Reconstruction of Baie des Baradères, Haiti

Moser, Sydney 11 November 2021 (has links)
In the Circum-Caribbean, long timescale paleoenvironmental records, which are used to establish important baseline data for climatological phenomena such as droughts, floods, and hurricanes, are rare. This project uses geological and geochemical proxies (X-Ray fluorescence core scanning data, grain-size analysis, loss on ignition) from a nine-meter-long sediment core from a coastal karst basin near Haiti’s southwestern peninsula to reconstruct Holocene environmental and climatic changes in a region that is both understudied and highly sensitive to the effects of storms, sea level change, tectonics, and anthropogenic impacts. The chronology of the core is established with 4 AMS dates from terrestrial organic matter and shows continuous sedimentation from ~8500 cal BP to the present, with an abrupt increase in sedimentation rate at ~2900 cal BP. High values of Ti and Ti/Ca are associated with finer sediments in the core and indicate relatively humid conditions at ~6500 cal BP, followed by a gradual drying trend. This is consistent with data from elsewhere in the Caribbean that reflects a southward migration of the ITCZ during the early Holocene. After 2500 cal BP, a series of large and abrupt increases in Ti and Ti/Ca are associated with an influx of finer, terrestrially-derived sediment into the bay due to enhanced discharge from the nearby Baradères River, possibly as a result of short-duration shifts to wetter climate conditions, hurricane-induced precipitation, and/or prehistoric-era human settlement. Variations in silicate input (e.g., K/Ti), marine productivity (e.g., Ca/Ti), and redox conditions (e.g., Mn/Fe) are linked to local climate changes and resulting changes in the depositional environment, while peaks in Rb/Sr and Ti/Ca could be signals for erosion related to events such as hurricanes and/or land use changes. Finally, high values of silt and clay, in conjunction with enhanced organics, Ti, Fe, K and P over the last couple centuries reflect historic-era deforestation and erosion. This study presents an excellent opportunity to further our understanding of the diverse relationships between ecosystem dymanics, climate, and anthropogenic forcings and adds to the growing inventory of paleoclimatological records in the Caribbean, improving the spatial distribution of such studies, and ultimately improving our understanding of the driving forces of both short- and long-term climate variability.
2

Assessment of Machine Learning Applied to X-Ray Fluorescence Core Scan Data from the Zinkgruvan Zn-Pb-Ag Deposit, Bergslagen, Sweden

Simán, Frans Filip January 2020 (has links)
Lithological core logging is a subjective and time consuming endeavour which could possibly be automated, the question is if and to what extent this automation would affect the resulting core logs. This study presents a case from the Zinkgruvan Zn-Pb-Ag mine, Bergslagen, Sweden; in which Classification and Regression Trees and K-means Clustering on the Self Organising Map were applied to X-Ray Flourescence lithogeochemistry data derived from automated core scan technology. These two methods are assessed through comparison to manual core logging. It is found that the X-Ray Fluorescence data are not sufficiently accurate or precise for the purpose of automated full lithological classification since not all elements are successfully quantified. Furthermore, not all lithologies are possible to distinquish with lithogeochemsitry alone furter hindering the success of automated lithological classification. This study concludes that; 1) K-means on the Self Organising Map is the most successful approach, however; this may be influenced by the method of domain validation, 2) the choice of ground truth for learning is important for both supervised learning and the assessment of machine learning accuracy and 3) geology, data resolution and choice of elements are important parameters for machine learning. Both the supervised method of Classification and Regression Trees and the unsupervised method of K-means clustering applied to Self Organising Maps show potential to assist core logging procedures.

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