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Donald A. Singer

Publications and source records attributed to Donald A. Singer.

At least 37 records · Page 2Linked to original sources
USGS2003

Typing mineral deposits using their grades and tonnages in an artificial neural network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further. Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage). Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag. Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Natural Resources Research↗
USGS2003

Sediment-hosted copper deposits of the world: Deposit models and database

Introduction This publication contains four descriptive models and four grade-tonnage models for sediment hosted copper deposits. Descriptive models are useful in exploration planning and resource assessment because they enable the user to identify deposits in the field and to identify areas on geologic and geophysical maps where deposits could occur. Grade and tonnage models are used in resource assessment to predict the likelihood of different combinations of grades and tonnages that could occur in undiscovered deposits in a specific area. They are also useful in exploration in deciding what deposit types meet the economic objectives of the exploration company. The models in this report supersede the sediment-hosted copper models in USGS Bulletin 1693 (Cox, 1986, and Mosier and others, 1986) and are subdivided into a general type and three subtypes. The general model is useful in classifying deposits whose features are obscured by metamorphism or are otherwise poorly described, and for assessing regions in which the geologic environments are poorly understood. The three subtypes are based on differences in deposit form and environments of deposition. These differences are described under subtypes in the general model. Deposit models are based on the descriptions of geologic environments and physical characteristics, and on metal grades and tonnages of many individual deposits. Data used in this study are presented in a database representing 785 deposits in nine continents. This database was derived partly from data published by Kirkham and others (1994) and from new information in recent publications. To facilitate the construction of grade and tonnage models, the information, presented by Kirkham in disaggregated form, was brought together to provide a single grade and a single tonnage for each deposit. Throughout the report individual deposits are defined as being more than 2,000 meters from the nearest adjacent deposit. The deposit models are presented here as a PDF file. The database can be most conveniently read in FileMaker Pro. For those who do not have the FileMaker application, Microsoft-Excel, tab-delimited-ASCII and comma-separated-value files are included. The reader may be interested in a similar publication on porphyry copper deposits (Singer and others, 2005) also available online. The Google Earth image is not intended to be viewed at the highest possible magnification because the resolution of the database is plus or minus two kilometers. At extreme zoom settings, the deposit locations may not coincide with the Google-Earth images of the mine workings.

Open-File Report↗
USGS2002

Porphyry copper deposits of the world: Database, maps, and preliminary analysis

Mineral deposit models are important in exploration planning and quantitative resource assessments for two reasons: (1) grades and tonnages among deposit types are significantly different, and (2) many types occur 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. Far too few thoroughly explored mineral deposits are available in most local areas for reliable identification of the important geoscience variables or for robust estimation of undiscovered deposits—thus we need mineral-deposit models. Globally based deposit models, such as those presented here, allow recognition of important features because the global models demonstrate how common different features are. Well-designed and -constructed deposit models allow geologists to know from observed geologic environments the possible mineral deposit types that might exist, and allow economists to determine the possible economic viability of these resources in the region. Thus, mineral deposit models play the central role in transforming geoscience information to a form useful to policy makers. The foundation of mineral deposit models is information about known deposits—the purpose of this publication is to make this kind of information available in digital form for a group of porphyry copper deposits. This publication contains a computer file of information on porphyry copper deposits around the world. It also presents new grade and tonnage models for three subtypes of porphyry copper deposits, maps showing locations and general ages of these deposits, and a preliminary analysis with a number of figures summarizing many of the properties of these porphyry-style deposits. These summaries can be considered a new, quantified, form of most parts of descriptive models such as those in Cox and Singer (1986). The value of this information and analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules used in this compilation. Next the fields of the data file are considered. Finally, we discuss some of the things that can be done with the data.

Open-File Report↗
USGS2002

Abstracts for the symposium on the application of neural networks to the earth sciences

Artificial neural networks are a group of mathematical methods that attempt to mimic some of the processes in the human mind. Although the foundations for these ideas were laid as early as 1943 (McCulloch and Pitts, 1943), it wasn't until 1986 (Rumelhart and McClelland, 1986; Masters, 1995) that applications to practical problems became possible. It is the acknowledged superiority of the human mind at recognizing patterns that the artificial neural networks are trying to imitate with their interconnected neurons. Interconnections used in the methods that have been developed allow robust learning. Capabilities of neural networks fall into three kinds of applications: (1) function fitting or prediction, (2) noise reduction or pattern recognition, and (3) classification or placing into types. Because of these capabilities and the powerful abilities of artificial neural networks, there have been increasing applications of these methods in the earth sciences. The abstracts in this document represent excellent samples of the range of applications. Talks associated with the abstracts were presented at the Symposium on the Application of Neural Networks to the Earth Sciences: Seventh International Symposium on Mineral Exploration (ISME–02), held August 20–21, 2002, at NASA Moffett Field, Mountain View, California. This symposium was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the U.S. Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations have always been wide-ranging—abstracts presented here are no exception.

Open-File Report↗
USGS2001

A simplified economic filter for open-pit mining and heap-leach recovery of copper in the United States

Determining the economic viability of mineral deposits of various sizes and grades is a critical task in all phases of mineral supply, from land-use management to mine development. This study evaluates two simple tools for estimating the economic viability of porphyry copper deposits mined by open-pit, heap-leach methods when only limited information on these deposits is available. These two methods are useful for evaluating deposits that either (1) are undiscovered deposits predicted by a mineral resource assessment, or (2) have been discovered but for which little data has been collected or released. The first tool uses ordinary least-squared regression analysis of cost and operating data from selected deposits to estimate a predictive relationship between mining rate, itself estimated from deposit size, and capital and operating costs. The second method uses cost models developed by the U.S. Bureau of Mines (Camm, 1991) updated using appropriate cost indices. We find that the cost model method works best for estimating capital costs and the empirical model works best for estimating operating costs for mines to be developed in the United States.

Open-File Report↗
USGS2001

Sedimentology of the Pennsylvanian and Permian Strathearn Formation, Northern Carlin Trend, Nevada; with a section on microfossil controls on age of the Strathearn Formation

Two framework-supported, poorly bedded conglomerate units of the middle Upper Pennsylvanian and middle Lower Permian Strathearn Formation belonging to the overlap assemblage of the Antler orogen are prominent in the northern Carlin trend. These horizons stratigraphically and temporally bracket thrust emplacement of a major allochthonous thrust plate of mainly quartzarenite of the Ordovician Vinini Formation. Lithologic and shape-ratio data from approximately 4,200 pebbles and cobbles at 17 sites as well as biostratigraphic data in the Strathearn, and their geologic implications, are included in this report. Conodont biofacies throughout the Strathearn Formation are normal marine and suggest middle shelf or deeper depositional environments. The conglomerate units roughly are similar in that they contain only chert and quartzarenite pebbles, but they differ in compositional proportions of the two lithologies. The relative proportion of quartzarenite pebbles increases sixfold in the middle Lower Permian upper conglomerate unit versus its content in the middle Upper Pennsylvanian lower unit, whereas chert pebbles predominate in both units. Various roundness categories of chert pebbles in both conglomerate units of the Strathearn show that the equant pebble class (B/A) = 1 clearly is represented strongly even in the subangular category, the lowest roundness categories for the pebbles. Thus, development of equant pebbles cannot be ascribed totally to a rounding process during predeposition transport. The equant character of many pebbles might, in part, be an original feature inherited from pre-erosion rock fractures and (or) bedding that control overall form of the fragments prior to their release to the transport environment. The allochthon of the Coyote thrust has been thrust above the lower conglomerate unit of the Strathearn during a regionally extensive contractional event in the late Paleozoic. The middle Lower Permian upper conglomerate unit, highest unit recognized in the Strathearn Formation, as well as similarly-aged dolomitic siltstone, onlap directly onto quartzarenite that comprises the allochthon of the Coyote thrust. The conglomerate units thus represent submarine fanglomerates whose quartz grains and quartzarenite fragments of variable roundness and shape were derived from a sedimentologically restored largely southeastward advancing late Paleozoic allochthonous lobe of mostly quartzarenite of the Ordovician Vinini Formation. Chert fragments in the conglomerates probably were derived mostly from Devonian Slaven Chert, including a widespread thick melange unit of the Slaven in the footwall of the Coyote thrust. Some chert pebbles may have been derived from the Ordovician Vinini Formation.

Nevada↗
USGS1999

A comparison of the weights-of-evidence method and probabilistic neural networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

Natural Resources Research↗
USGS1999

Examining risk in mineral exploration

Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin . Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.

Natural Resources Research↗
USGS1999

Geochemistry of stream-sediment samples from the Santa Renia Fields and Beaver Peak quadrangles, northern Carlin Trend, Nevada

A broad west-to-east increase of many metal concentrations has been found in stream sediments during a reconnaissance investigation conducted in conjunction with geologic studies in the Santa Renia Fields and Beaver Peak 7–1/2 minute quadrangles near the northern end of the Carlin trend of gold deposits in the Tuscarora Mountains. This regional increase in metal concentrations coincides with a dramatic change in landform wherein high concentrations of metals in stream sediments appear to correlate directly with areas of high elevations and steep slopes in the Beaver Peak quadrangle. Robust erosion combined with high flow rates in streams from these higher elevations are envisaged to have contributed significantly to increased metal concentrations in the stream sediments by an enhanced presence of minerals with high specific gravities and a correspondingly diminished presence of minerals with low specific gravities. Minerals with low specific gravities probably have been preferentially flushed down stream because of high transporting capacities for sediment by streams in the Beaver Peak quadrangle. In addition, the Carlin trend, a generally northwest-alignment of gold deposits in the Santa Renia Fields quadrangle, is well outlined by arsenic concentrations that include a maximum of approximately 54 parts per million. Further, a weakly developed distal-to-proximal metal zonation towards these gold deposits appears to be defined respectively in plots showing distributions of thallium, arsenic, antimony, and zinc. A broad area of high metal concentrations—including sharply elevated abundances of Ag, As, Au, Cd, Co, Cu, Mn, Ni, P, Sb, Sc, Te, V, and especially Zn—near the southeast corner of the Beaver Peak quadrangle primarily could be the result of stratiform mineralized rocks in the Ordovician Vinini Formation or Devonian Slaven Chert, or the result of a subsequent Mesozoic or Tertiary epigenetic overprint.

Nevada↗
USGS1998

A simplified economic filter for open-pit gold-silver mining in the United States

In resource assessments of undiscovered mineral deposits and in the early stages of exploration, including planning, a need for prefeasibility cost models exists. In exploration, these models to filter economic from uneconomic deposits help to focus on targets that can really benefit the exploration enterprise. In resource assessment, these models can be used to eliminate deposits that would probably be uneconomic even if discovered. The U. S. Bureau of Mines (USBM) previously developed simplified cost models for such problems (Camm, 1991). These cost models estimate operating and capital expenditures for a mineral deposit given its tonnage, grade, and depth. These cost models were also incorporated in USBM prefeasibility software (Smith, 1991). Because the cost data used to estimate operating and capital costs in these models are now over ten years old, we decided that it was necessary to test these equations with more current data. We limited this study to open-pit gold-silver mines located in the United States.

Open-File Report↗
USGS1997

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Nonrenewable Resources↗
USGS1996

Application of a feedforward neural network in the search for kuroko deposits in the Hokuroku District, Japan

A feedforward neural network with one hidden layer and five neurons was trained to recognize the distance to kuroko mineral deposits. Average amounts per hole of pyrite, sericite, and gypsum plus anhydrite as measured by X-rays in 69 drillholes were used to train the net. Drillholes near and between the Fukazawa, Furutobe, and Shakanai mines were used. The training data were selected carefully to represent well-explored areas where some confidence of the distance to ore was assured. A logarithmic transform was applied to remove the skewness of distance and each variable was scaled and centered by subtracting the median and dividing by the interquartile range. The learning algorithm of annealing plus conjugate gradients was used to minimize the mean squared error of the scaled distance to ore. The trained network then was applied to all of the 152 drillholes that had measured gypsum, sericite, and pyrite. A contour plot of the neural net predicted distance to ore shows fairly wide areas of 1 km or less to ore; each of the known deposit groups is within the 1 km contour. The high and low distances on the margins of the contoured distance plot are in part the result of boundary effects of the contouring algorithm. For example, the short distances to ore predicted west of the Shakanai (Hanaoka) deposits are in basement. However, the short distances to ore predicted northeast of Furotobe, just off the figure, coincide with the location of the Nurukawa kuroko deposit and the Omaki deposit, south of the Shakanai-Hanaoka deposits, seems to be on an extension of short distance to ore contour, but is beyond the 3 km limit from drillholes. Also of interest are some areas only a few kilometers from the Fukazawa and Shakanai groups of deposits that are estimated to be many kilometers from ore, apparently reflecting the network's recognition of the extreme local variability of the geology near some deposits.

Mathematical Geology↗