The competition in the world market for iron ore has increased lately. Therefore, an improved method of estimating the ore quality in small working areas has become an attractive cost-cutting strategy in short-term mine plans. Estimated grades of different working areas of a mine form the basis of any short-term mine plan. The generally sparse exploration data obtained during the development phase is not enough to accurately estimate the grades of small working areas. Therefore, additional sample information is often required in any operating mine. The findings of this case study show that better utilization of all available exploration information at this mine would improve estimation of small working areas even without additional face samples. Through the use of kriging variance, this study also determined the optimum face sampling grid, whose spacing turned out to be approximately 100 meters as compared to 50 meters in use today. (Abstract shortened with permission of author.)

No description available.

Thesis (Ph. D.)--University of California, San Diego, 1998. / Vita. Includes bibliographical references (leaves 114-116).

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 86-89). Available also in a digital version from Dissertation Abstracts.

No description available.

This study introduces a new methodology referred to as geoinformation synthesis for multivariate evaluation of mineral resources and integration of diverse geoscience data. The most critical component is the development of the notion of intrinsic samples and the methods for their delineation. Intrinsic samples replace grid cells which are conventionally employed as the basic information reference. Grid cell sampling has imposed several serious limitations on the geoscience and genetic information that can be objectively related to mineral endowment. Methods based upon intrinsic samples moderate to a certain extent these problems and bring the critical genetic information into the geoscience information system which forms the basis for the quantitative evaluation of mineral resources. The second major component in this new methodology is the integration of factors describing exploration effects with other geodata and mineral endowment estimation; this combination effectively reduces the possibilities of biases in the estimates of mineral endowment and recoverable resources due to the incomplete knowledge on the control area and imperfect analogy with the study areas. The third component is the use in the qualitative models of synthesized geoinformation, which is considerably enhanced, instead of using directly the original measurements (geodata). Several multivariate techniques are proposed and employed for synthesis of diverse information and estimation of mineral endowment, including a priori weighted multivariate criterion, optimum discretization, coherency analysis, multidimensional scaling method (p(ijk), filtering analysis, and geochemical transportation models. These methods were developed, tested, and demonstrated on an actual case study of the epithermal gold-silver deposits in the Walker Lake quadrangle of Nevada and California using various data sets available for this region: geochemical, structural, gravity and magnetic, lithology, and alteration. Finally, the estimation of endowment in terms of epithermal gold-silver mineral occurrences is given for some selected intrinsic samples or information zones identified in the Walker Lake region.

No description available.

M.Sc. / Please refer to full text to view abstract

No description available.

Recent growth of the Internet in both scale and complexity has imposed a number of difficult challenges on existing measurement techniques and approaches, which are essential for both network management and many ongoing research projects. For any measurement algorithm, achieving both accuracy and scalability is very challenging given hard resource constraints (e.g., bandwidth, delay, physical memory, and CPU speed). My dissertation research tackles this problem by first proposing a novel mechanism called residual sampling, which intentionally introduces a predetermined amount of bias into the measurement process. We show that such biased sampling can be extremely scalable; moreover, we develop residual estimation algorithms that can unbiasedly recover the original information from the sampled data. Utilizing these results, we further develop two versions of the residual sampling mechanism: a continuous version for characterizing the user lifetime distribution in large-scale peer-to-peer networks and a discrete version for monitoring flow statistics (including per-flow counts and the flow size distribution) in high-speed Internet routers. For the former application in P2P networks, this work presents two methods: ResIDual-based Estimator (RIDE), which takes single-point snapshots of the system and assumes systems with stationary arrivals, and Uniform RIDE (U-RIDE), which takes multiple snapshots and adapts to systems with arbitrary (including non-stationary) arrival processes. For the latter application in traffic monitoring, we introduce Discrete RIDE (D-RIDE), which allows one to sample each flow with a geometric random variable. Our numerous simulations and experiments with P2P networks and real Internet traces confirm that these algorithms are able to make accurate estimation about the monitored metrics and simultaneously meet the requirements of hard resource constraints. These results show that residual sampling indeed provides an ideal solution to balancing between accuracy and scalability.