Energy- and mineral-resource assessments—How are they done? Who are they for? How effective are they?     

Richard B. McCammon  W. David Menzie III  John H. Schuenemeyer 《Natural Resources Research》1992,1(1):5-38

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Book reviews     

John H. Schuenemeyer  Richard M. Procter  William N. Szymanski 《Natural Resources Research》1992,1(1):107-108

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Book review     

Martin A. Buzas  Michael Ed. Hohn  John H. Schuenemeyer 《Mathematical Geology》1986,18(2):257-262

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Investigation of the Structure of Geological Process through Multivariate Statistical Analysis—The Creation of a Coal     

Lawrence J. Drew  Eric C. Grunsky  John H. Schuenemeyer 《Mathematical Geosciences》2008,40(7):789-811

The purpose of this study was to capture the structure of a geological process within a multivariate statistical framework by using geological data generated by that process and, where applicable, by associated processes. It is important to the practitioners of statistical analysis in geology to determine the degree to which the geological process can be captured and explained by multivariate analysis by using sample data (for example, chemical analyses) taken from the geological entity created by that process. The process chosen for study here is the creation of a coal deposit. In this study, the data are chemical analyses expressed in weight percentage and parts per million, and therefore are subject to the affects of the constant sum phenomenon. The data array is the chemical composition of the whole coal. This restriction on the data imposed by the constant sum phenomenon was removed by using the centered logratio (clr) transformation. The use of scatter plots and principal component biplots applied to the raw and centered logratio (clr) transformed data arrays affects the interpretation and comprehension of the geological process of coalification.  相似文献   

A Framework for Quantitative Assessment of Impacts Related to Energy and Mineral Resource Development     

Seth S. Haines  Jay E. Diffendorfer  Laurie Balistrieri  Byron Berger  Troy Cook  Don DeAngelis  Holly Doremus  Donald L. Gautier  Tanya Gallegos  Margot Gerritsen  Elisabeth Graffy  Sarah Hawkins  Kathleen M. Johnson  Jordan Macknick  Peter McMahon  Tim Modde  Brenda Pierce  John H. Schuenemeyer  Darius Semmens  Benjamin Simon  Jason Taylor  Katie Walton-Day 《Natural Resources Research》2014,23(1):3-17

Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.  相似文献