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D.A. Singer

Publications and source records attributed to D.A. Singer.

At least 19 recordsLinked to original sources
USGS2008

Map scale effects on estimating the number of undiscovered mineral deposits

Estimates of numbers of undiscovered mineral deposits, fundamental to assessing mineral resources, are affected by map scale. Where consistently defined deposits of a particular type are estimated, spatial and frequency distributions of deposits are linked in that some frequency distributions can be generated by processes randomly in space whereas others are generated by processes suggesting clustering in space. Possible spatial distributions of mineral deposits and their related frequency distributions are affected by map scale and associated inclusions of non-permissive or covered geological settings. More generalized map scales are more likely to cause inclusion of geologic settings that are not really permissive for the deposit type, or that include unreported cover over permissive areas, resulting in the appearance of deposit clustering. Thus, overly generalized map scales can cause deposits to appear clustered. We propose a model that captures the effects of map scale and the related inclusion of non-permissive geologic settings on numbers of deposits estimates, the zero-inflated Poisson distribution. Effects of map scale as represented by the zero-inflated Poisson distribution suggest that the appearance of deposit clustering should diminish as mapping becomes more detailed because the number of inflated zeros would decrease with more detailed maps. Based on observed worldwide relationships between map scale and areas permissive for deposit types, mapping at a scale with twice the detail should cut permissive area size of a porphyry copper tract to 29% and a volcanic-hosted massive sulfide tract to 50% of their original sizes. Thus some direct benefits of mapping an area at a more detailed scale are indicated by significant reductions in areas permissive for deposit types, increased deposit density and, as a consequence, reduced uncertainty in the estimate of number of undiscovered deposits. Exploration enterprises benefit from reduced areas requiring detailed and expensive exploration, and land-use planners benefit from reduced areas of concern. ?? 2008 International Association for Mathematical Geology.

Natural Resources Research↗
USGS2006

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Mathematical Geology↗
USGS2004

Synthrusting deposition of the Pennsylvanian and Permian Strathearn Formation, Northern Carlin Trend, Nevada

The middle Upper Pennsylvanian and middle Lower Permian Strathearn Formation belongs to the overlap assemblage of the Antler orogen in Nevada. At Beaver Peak, near the Carlin Trend of gold deposits, it contains synorogenic conglomerate deposits associated with emplacement of a regionally extensive, 1-km-thick tectonic wedge that is floored by the Coyote thrust. Normal marine conodont biofacies throughout the Strathearn Formation suggest middle shelf or deeper, depositional environments. The allochthon floored by the Coyote thrust has been thrust above a middle Upper Pennsylvanian, lower conglomerate unit of the Strathearn Formation. A middle Lower Permian upper conglomerate unit, the highest unit recognized in the Strathearn Formation, as well as similarly aged dolomitic siltstone, onlap directly onto Ordovician quartzarenite of the Vinini Formation that makes up most of the Coyote allochthon. Quartz grains and quartzarenite fragments of variable roundness and shape in the conglomerate units were derived from the presently adjoining tectonic lobe of mostly quartzarenite that advanced southeast (present geographic coordinates) during the late Paleozoic into the developing Strathearn basin. Chert fragments in the conglomerates probably were derived mostly from Devonian Slaven Chert, including a widespread thick me??lange unit of the Slaven Chert in the footwall of the Coyote thrust.Lithologic and shape ratio data from approximately 4200 clasts at 17 sites of the two major conglomerate units in the Strathearn Formation at Beaver Peak are roughly similar in that they contain only chert and quartzarenite clasts, and chert clasts predominate in both units. They differ in the relative proportion of the two lithologies whereby quartzarenite clasts increase sixfold in the upper unit (middle Lower Permian) versus its content in the lower conglomerate unit. Relations at the unconformity between the upper conglomerate unit and its underlying quartzarenite shows quartzarenite fragments actually breaking away from an immediately subjacent source. Ordovocian quartzarenite, which forms a tectonically uplifted wedge with the Coyote thrust at its base, became a source region for much of the quartzarenite detritus deposited preferentially in the upper parts of the Strathearn Formation. The conglomerate units of the Strathearn Formation temporally bracket emplacement of the Coyote thrust. Thrusting related to contractional reactivation of the Robert Mountains thrust system largely was completed by middle Early Permian. ?? 2004 Published by Elsevier B.V.

Sedimentary Geology↗
USGS2003

Applied geochemistry, geology and mineralogy of the northernmost Carlin trend, Nevada

Investigations in the northernmost Carlin trend were undertaken to advance understanding of the geochemical signatures and genesis of precious metal deposits in the trend . Two fundamental geologic relationships near the trend significantly affect regional geochemical distributions: a remarkably intact lower Paleozoic stratigraphic sequence of siliceous rocks in the upper plate of the middle Paleozoic Roberts Mountains thrust, and the widespread repetition of rocks high in the upper plate during late Paleozoic thrusting that thickens the cover above mineralized rock in the lower plate. A compilation of previously published chemical analyses of 440 stream sediment samples and 115 rocks from two 7 1/2-minute quadrangles, as well as new chemical analyses of approximately 1,000 drill core samples in a 1,514 m (4,970 ft) hole through the Rodeo Creek deposit were used to construct three-dimensional element distribution models that highlight metal zonation in the mineralized systems. The Rodeo Creek deposit comprises deep Ag base-metal ± Au-mineralized rock below the Roberts Mountains thrust and contains an unusually high Ag/Au ratio greater than 30. Stacked geochemical halos related to the deposit are confined to the lower plate of the Roberts Mountains thrust and include two horizons of Hg, Cu, and Zn anomalies-as much as 180 m above the deposit-that mostly result from mercurian sphalerite. Extremely subtle indications of mineralization in the upper plate of the Roberts Mountains thrust above the deposit include arsenopyrite overgrowths on small pyrite crystals in 50- to 75-μm-wide clay-carbonate veinlets that lack alteration halos, arsenical rims on small disseminated crystal of recrystallized diagenetic pyrite, and partial replacement of diagenetic pyrite by tennantite. Some of these minerals contain anomalously high Au. However, these As-(Au)-bearing rocks most likely represent another locus of largely untested mineralized rock rather than distal halos related to either the Rodeo Creek or the nearby Dee and Storm gold deposits. Application of micromineralogic techniques helped to identify mineral assemblages that are specific to mineralization and provided an empirical foundation for interpretations of geochemical halos in the Carlin trend . District-scale geochemical patterns of several elements in stream sediments and surface rocks coincide with the northernmost Carlin trend and can be used to explore for Carlin -type deposits. Concentrations of elevated As and Sb in stream sediments (as much as 54 ppm As) have northwest-elongate lobate patterns that clearly outline the trend across a width of approximately 4 km. Arsenic contents of exposed rocks (as much as 90 ppm As) strongly correlate with As contents of derivative stream sediments, and rock contents of Sb show a somewhat lesser but nonetheless strong and similar correspondence. Factor analysis of stream-sediment data shows that those factor scores that are correlated with As, Sb, Au, and Pb also are high along the trend and suggest that mineralized rocks may be present. Although As was not detected by scanning electron microscope-energy dispersive spectrometer (SEM-EDS) studies in heavy mineral concentrates of high-As stream sediments in the Carlin trend , X-ray absorption near-edge spectra (XANES) of selected light fractions of stream sediment samples indicate that Al-bearing phases, such as gibbsite, amorphous Al oxyhydroxides, or aluminosilicate clay minerals host most of the As(V). The best fit, visually and in terms of the lowest residual, was obtained by a model compound of As(V) sorbed to gibbsite. Thus, most As in stream sediments derived from altered rock within the Carlin trend apparently is contained in light fractions. The geochemical character of young, unconsolidated, postmineral deposits that cover mineralized rocks on the Carlin trend partly results from mineralized sources along the trend . Concentration of As in the Miocene Carlin Formation shows an exceptionally well developed progressive increase to about 30 ppm As as altered rock surrounding the trend is approached. Mineralized and/or altered rock fragments probably have been shed directly into the sedimentary basin of the Carlin Formation, and migration of As, now fixed as As(V), also may have occurred in the supergene environment after material was recycled out of the Carlin Formation and into present-day gulleys.

Nevada↗
USGS2001

Some simple guides to finding useful information in exploration geochemical data

Most regional geochemistry data reflect processes that can produce superfluous bits of noise and, perhaps, information about the mineralization process of interest. There are two end-member approaches to finding patterns in geochemical data—unsupervised learning and supervised learning. In unsupervised learning, data are processed and the geochemist is given the task of interpreting and identifying possible sources of any patterns. In supervised learning, data from known subgroups such as rock type, mineralized and nonmineralized, and types of mineralization are used to train the system which then is given unknown samples to classify into these subgroups. To locate patterns of interest, it is helpful to transform the data and to remove unwanted masking patterns. With trace elements use of a logarithmic transformation is recommended. In many situations, missing censored data can be estimated using multiple regression of other uncensored variables on the variable with censored values. In unsupervised learning, transformed values can be standardized, or normalized, to a Z-score by subtracting the subset's mean and dividing by its standard deviation. Subsets include any source of differences that might be related to processes unrelated to the target sought such as different laboratories, regional alteration, analytical procedures, or rock types. Normalization removes effects of different means and measurement scales as well as facilitates comparison of spatial patterns of elements. These adjustments remove effects of different subgroups and hopefully leave on the map the simple and uncluttered pattern(s) related to the mineralization only. Supervised learning methods, such as discriminant analysis and neural networks, offer the promise of consistent and, in certain situations, unbiased estimates of where mineralization might exist. These methods critically rely on being trained with data that encompasses all populations fairly and that can possibly fall into only the identified populations.

Natural Resources Research↗
USGS2001

Some suggested future directions of quantitative resource assessments

Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, those affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because the dominance of tonnage-deposit models are the best known predictor of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.

Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Sc↗
USGS1994

Using PETRIMES to estimate mercury deposits in California

In this article, we examine the use of an unconventional procedure, PETRIMES, to estimate mineral resources of mercury deposits in California. The study, which is based on the nonparametric discovery process model and Q-Q plots, suggests that a lognormal distribution is appropriate for the mercury deposits in California. The results of the assessment are summarized as follows: (1) the total number of mercury deposits in the population is approximately 165; (2) the median value of the largest undiscovered deposit size is 487 flasks; (3) the mean of the remaining mercury potential is 2,500 flasks; and (4) the population resource ranges from 1,040,000 to 4,300,000 flasks (at a 0.9 probability level). ?? 1994 Oxford University Press.

Nonrenewable Resources↗
USGS1994

Conditional estimates of the number of podiform chromite deposits

A desirable guide for estimating the number of undiscovered mineral deposits is the number of known deposits per unit area from another well-explored permissive terrain. An analysis of the distribution of 805 podiform chromite deposits among ultramafic rocks in 12 subareas of Oregon and 27 counties of California is used to examine and extend this guide. The average number of deposits in this sample of 39 areas is 0.225 deposits per km2 of ultramafic rock; the frequency distribution is significantly skewed to the right. Probabilistic estimates can be made by using the observation that the lognormal distribution fits the distribution of deposits per unit area. A further improvement in the estimates is available by using the relationship between the area of ultramafic rock and the number of deposits. The number (N) of exposed podiform chromite deposits can be estimated by the following relationship: log10(N)=-0.194+0.577 log10(area of ultramafic rock). The slope is significantly different from both 0.0 and 1.0. Because the slope is less than 1.0, the ratio of deposits to area of permissive rock is a biased estimator when the area of ultramafic rock is different from the median 93 km2. Unbiased estimates of the number of podiform chromite deposits can be made with the regression equation and 80 percent confidence limits presented herein. ?? 1994 Oxford University Press.

Nonrenewable Resources↗
USGS1993

Basic concepts in three-part quantitative assessments of undiscovered mineral resources

Since 1975, mineral resource assessments have been made for over 27 areas covering 5??106 km2 at various scales using what is now called the three-part form of quantitative assessment. In these assessments, (1) areas are delineated according to the types of deposits permitted by the geology,(2) the amount of metal and some ore characteristics are estimated using grade and tonnage models, and (3) the number of undiscovered deposits of each type is estimated. Permissive boundaries are drawn for one or more deposit types such that the probability of a deposit lying outside the boundary is negligible, that is, less than 1 in 100,000 to 1,000,000. Grade and tonnage models combined with estimates of the number of deposits are the fundamental means of translating geologists' resource assessments into a language that economists can use. Estimates of the number of deposits explicitly represent the probability (or degree of belief) that some fixed but unknown number of undiscovered deposits exist in the delineated tracts. Estimates are by deposit type and must be consistent with the grade and tonnage model. Other guidelines for these estimates include (1) frequency of deposits from well-explored areas, (2) local deposit extrapolations, (3) counting and assigning probabilities to anomalies and occurrences, (4) process constraints, (5) relative frequencies of related deposit types, and (6) area spatial limits. In most cases, estimates are made subjectively, as they are in meteorology, gambling, and geologic interpretations. In three-part assessments, the estimates are internally consistent because delineated tracts are consistent with descriptive models, grade and tonnage models are consistent with descriptive models, as well as with known deposits in the area, and estimates of number of deposits are consistent with grade and tonnage models. All available information is used in the assessment, and uncertainty is explicitly represented. ?? 1993 Oxford University Press.

Nonrenewable Resources↗
USGS1992

The Conterminous United States Mineral Assessment Program: Background information to accompany folio of geologic, geochemical, geophysical, and mineral resource maps of the Ajo and Lukeville 1° by 2° quadrangles, Arizona

Encompassing about 21,000 km 2 in southwestern Arizona, the Ajo and Lukeville 1° by 2° quadrangles have been the subject of mineral resource investigations utilizing field and laboratory studies in the disciplines of geology, geochemistry, geophysics, and Landsat imagery. The results of these studies are published as a folio of maps, figures, and tables, with accompanying discussions. Past mineral production has been limited to copper from the Ajo Mining District. In addition to copper, the quadrangles contain potentially significant resources of gold and silver; a few other commodities, including molybdenum and evaporites, may also exist in the area as appreciable resources. This circular provides background information on the mineral deposits and on the investigations and integrates the information presented in the folio. The bibliography cites references to the geology, geochemistry, geophysics, and mineral deposits of the two quadrangles.

Arizona↗