Investigation of Mantle Dynamics from Platinum Group Elements and Rhenium-Osmium Isotope Systematics of Mantle Xenoliths from Oahu, Hawaii

Intraplate volcanism that has created the Hawaiian-Emperor seamount chain is generally thought to be formed by a deep-seated mantle plume. While the idea of a Hawaiian plume has not met with substantial opposition, whether or not the Hawaiian plume shows any geochemical signal of receiving materials from the Earth’s Outer Core and how the plume may or may not be reacting with the overriding lithosphere remain debatable issues. In an effort to understand how the Hawaiian plume works I report on the first in-situ sulfides and bulk rock Platinum Group Element (PGE) concentrations, together with Os isotope ratios on well-characterized garnet pyroxenite xenoliths from the island of Oahu in Hawaii. The sulfides are Fe-Ni Monosulfide Solid Solution and show fractionated PGE patterns. Based on the major elements, Platinum Group Elements and experimental data I interpret the Hawaiian sulfides as an immiscible melt that separated from a melt similar to the Honolulu Volcanics (HV) alkali lavas at a pressure-temperature condition of 1530 ± 100OC and 3.1±0.6 GPa., i.e. near the base or slightly below the Pacific lithosphere. The 187Os/188Os ratios of the bulk rock vary from subchondritic to suprachondritic (0.123-0.164); and the 187Os/188Os ratio strongly correlates with major element, High Field Strength Element (HFSE), Rare Earth Element (REE) and PGE abundances. These correlations strongly suggest that PGE concentrations and Os isotope ratios reflect primary mantle processes. I interpret these correlations as the result of melt-mantle reaction at the base of the lithosphere: I suggest that the parental melt that crystallized the pyroxenites selectively picked up radiogenic Os from the grain boundary sulfides, while percolating through the Pacific lithosphere. Thus the sampled pyroxenites essentially represent crystallized melts from different stages of this melt-mantle reaction process at the base of the lithosphere. I further show that the relatively low Pt/Re ratios of the Hawaiian sulfides and the bulk rock pyroxenites suggest that, upon ageing, such pyroxenites plus their sulfides cannot generate the coupled 186Os- 187Os isotope enrichments observed in Hawaiian lavas. Therefore, recycling of mantle sulfides of pyroxenitic parentage is unlikely to explain the enriched Pt-Re-Os isotope systematics of plume-derived lavas.

Derivation of lake mixing and stratification indices from high-resolution lake buoy data

Lake Analyzer is a numerical code coupled with supporting visualization tools for determining indices of mixing and stratification that are critical to the biogeochemical cycles of lakes and reservoirs. Stability indices, including Lake Number, Wedderburn Number, Schmidt Stability, and thermocline depth are calculated according to established literature definitions and returned to the user in a time series format. The program was created for the analysis of high-frequency data collected from instrumented lake buoys, in support of the emerging field of aquatic sensor network science. Available outputs for the Lake Analyzer program are: water temperature (error-checked and/or down-sampled), wind speed (error-checked and/or down-sampled), metalimnion extent (top and bottom), thermocline depth, friction velocity, Lake Number, Wedderburn Number, Schmidt Stability, mode-1 vertical seiche period, and Brunt-Väisälä buoyancy frequency. Secondary outputs for several of these indices delineate the parent thermocline depth (seasonal thermocline) from the shallower secondary or diurnal thermocline. Lake Analyzer provides a program suite and best practices for the comparison of mixing and stratification indices in lakes across gradients of climate, hydro-physiography, and time, and enables a more detailed understanding of the resulting biogeochemical transformations at different spatial and temporal scales.

Data partitioning and replication management in distributed geographic information system (GIS) database

This research presents several components encompassing the scope of the objective of Data Partitioning and Replication Management in Distributed GIS Database. Modern Geographic Information Systems (GIS) databases are often large and complicated. Therefore data partitioning and replication management problems need to be addresses in development of an efficient and scalable solution. ^ Part of the research is to study the patterns of geographical raster data processing and to propose the algorithms to improve availability of such data. These algorithms and approaches are targeting granularity of geographic data objects as well as data partitioning in geographic databases to achieve high data availability and Quality of Service(QoS) considering distributed data delivery and processing. To achieve this goal a dynamic, real-time approach for mosaicking digital images of different temporal and spatial characteristics into tiles is proposed. This dynamic approach reuses digital images upon demand and generates mosaicked tiles only for the required region according to user's requirements such as resolution, temporal range, and target bands to reduce redundancy in storage and to utilize available computing and storage resources more efficiently. ^ Another part of the research pursued methods for efficient acquiring of GIS data from external heterogeneous databases and Web services as well as end-user GIS data delivery enhancements, automation and 3D virtual reality presentation. ^ There are vast numbers of computing, network, and storage resources idling or not fully utilized available on the Internet. Proposed "Crawling Distributed Operating System "(CDOS) approach employs such resources and creates benefits for the hosts that lend their CPU, network, and storage resources to be used in GIS database context. ^ The results of this dissertation demonstrate effective ways to develop a highly scalable GIS database. The approach developed in this dissertation has resulted in creation of TerraFly GIS database that is used by US government, researchers, and general public to facilitate Web access to remotely-sensed imagery and GIS vector information. ^

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^