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analysis, and dissemination of information on major world deposits of selected strategic mineral commodities. Through this effort, the six participating nations obtain reliable information on production and resources that improves the basis for sound policy decisions, avoids duplication of effort, and encourages scientific exchange between countries. The study found that of the world's 23,000 metric tons of known economically minable resources of platinum-group metals, more than 98 percent are in South Africa and the Soviet Union. The U.S. and Canada combined have most of the remainder of the economic resources of these metals. Colombia and Zimbabwe also have economic deposits. Another 62,300 metric tons of platinum-group metals are estimated to occur in deposits that are uneconomic to recover or in less well-known extensions of identified deposits. Additional minor or less well-known deposits have been discovered in several countries including Australia, Brazil, Chile, China, Finland, and Zaire. The platinum-group metals are chemically inert, have high melting points and high ductility, and exhibit extraordinary catalytic activity. They are used in processing petroleum and other fossil fuels, in ammonium oxidation to produce nitric acid

for fertilizers, and in other chemical processing, as well as in emission controls for automobiles and industrial processes. Other uses are in fuel cells, electrical and electronic hardware, jewelry, and medical and dental applications. Over 95 percent of these metals consumed in the U.S. are used for industrial applications. To date, the scientific effort that has gone into finding substitutes has met with little success, and demand for platinum-group metals is likely to increase as a result of Australia, Europe, and South Korea implementing automobile emission standards similar to those in the United States. The world's most important deposits (fig. 8) of platinum-group metals are associated with magmatic intrusions of mafic or ultramafic rocks and fall in three main categories: stratiform deposits such as the Bushveld Complex in South Africa and the Stillwater Complex in the U.S.; a unique intrusion at Sudbury, Canada, which was probably initiated by the impact of a meteorite; and nickel-copper—bearing dikes and sills, which are found in association with rift structures such as Noril'sk-Talnakh district in the Soviet Union. Placer deposits, such as those in the Upper San Juan and Atrato Rivers in Colombia, in the past contributed a major part of platinum-group metal production. Stratiform deposits are

Stillwater Complex (1)

San Juan and Atrata

20* || Rivers district (1)

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Boundary and names representation not necessarily authoritative

soviet UNION
Noril'sk-Talnakh district (1)

Witwatersrand (1)

Geologic deposit type

Symbol Resources (metric tons)
O Unreported

Symbol Resources (metric tons)
L <1,000

Figure 8. Location, deposit type, and estimated resources of major platinum-group-metal deposits and districts in the world. Numbers in parentheses indicate number of deposits and districts for each location.


mined principally for their platinum-group metals, with nickel, copper, and cobalt being by-products. In other deposit types, except for some placers, platinum-group metals are byproducts of nickel and copper mining. The six countries having major platinum-group-metal deposits have accounted for 99.5 percent of world production of over 4,250 metric tons from 1735 to 1983 (the latest year for which international production statistics are available). The Soviet Union has accounted for nearly one-half of this production and 56 percent of the 202 metric tons of platinum-group metals marketed in 1983. Soviet sales do not reflect world demand but do reflect the relative independence of the Soviet Union in the world market at a particular time. About one-third of cumulative production and 41 percent of 1983 production took place in South Africa. Sharp increases in production from South Africa and the Soviet Union from 1940 to 1980 caused the proportion of platinum-group-metal production accounted for by other countries including Canada, Colombia, and the United States to fall from 53 percent in 1940 to 7 percent in 1980. Canada's proportion alone went from 43 percent in 1940 to 6 percent in 1980 despite a two-fold increase in output. On the basis of identified resources, it is apparent that through the year 2020, the Sudbury Complex, Noril'sk-Talnakh district, and Bushveld Complex will probably continue to be significant producers, and the Stillwater Complex will become a significant producer. However, continued exploration and development of deposits, such as the Penikat layered intrusion (Finland) and Jinchuan deposit (China), and the potential discovery of other stratiform deposits could dramatically change the supply situation by 2020.

Orthophotographs From Digitally Processed Images

By Leonard Gaydos

Orthophotographs combine the image qualities of a photograph with the geometric qualities of a map and serve a variety of purposes, from interim maps to field references for forest firefighters. Since 1985, the Survey has been developing a new method for producing orthophotographs using completely digital techniques.

A digital image is created by scanning a color-infrared aerial photograph through blue, green, and red filters on a film scanner. These data are matched with a digital elevation model of the same area. Computer software then rectifies the image by comparing measurements from the original photograph and the digital elevation model data to remove relief displacement. The software used is DPSOR (Digital Photogrammetric System Orthophoto), which is installed on a Gould 32/9780 computer at the Survey's Western Mapping Center in Menlo Park, California.

After rectification, the digital orthophotographs are further processed at the Survey's Earth Resources Observation Systems Data Center in Sioux Falls, South Dakota. There, the images are enhanced to improve color balance, and the adjoining digital orthophotographs are mosaicked together to complete map quadrangle coverage. The final product is generated on film, then enlarged on a copy camera to either 1:12,000 or 1:24,000 scale.

Research continues into the use of the technology in the map revision process and as high-resolution multispectral data sources for geographic information systems.

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Figure 9. Computer-generated map showing the results of modeling geological, geochemical, geophysical, and mineralresource data within a geographic information system to describe favorable environments for base- and preciousmetal mineralization in the Dillon, Montana-Idaho, 1°x2° quadrangle. The color-coded summary classifies areas as having very high (red), high (green), moderate (blue), or low (gray) potential for undiscovered vein-type mineral deposits.

Geographic Information Systems Aid Mineral


By John L. Place, Lawrence G. Batten, and Charles M. Trautwein

The U.S. Geological Survey has been automating its mapmaking procedures and in the process is creating base category data for inclusion in geographic information systems. A geographic information system is a computer hardware and software system designed to collect, manage, analyze, and display spatially referenced data. Data features may be points (wells, schools), lines (roads, streams), or polygons (counties, forests).

There has been a marked increase in the use of geographic information systems for interdivisional research within the Survey. Recently, a 5-year program of research, development, and application of geographic information systems for processing geoscience data was completed through a cooperative investigation conducted by the Survey's Geologic and

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National Mapping Divisions. The investigation required the definition and development of Sophisticated data processing algorithms to digitally compile, analyze, and combine the various geological, geochemical, and geophysical data that are used to assess mineral resources. The resulting technology was then used to evaluate several 1°X2° quadrangles selected from the Conterminous United States Mineral Appraisal Program. In each of the quadrangles, quantitative models based on the characteristics of different types of mineral deposits and spatial relationships between the digitally compiled data sets and known mineral occurrences were used to describe favorable areas for the occurrence of additional reserves (fig. 9). Geographic information systems are particularly useful in processing large amounts of geographically referenced earth-science data because of the systems’ ability to efficiently analyze, overlay, and graphically display a variety of related data. In the cooperative investigation, USGS scientists were able to develop and successfully demonstrate the systems’ automated, computer-based capabilities to (1) construct complex geoscience models that describe regional mineral resource potential; (2) apply mineral resource models to large, multivariate, digital data sets; (3) edit, modify, and reevaluate assessed mineral potential on the basis of updated or

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new types of geoscience data; and (4) gen-
erate products in digital (computer-
compatible), tabular, and cartographic
On the basis of these findings and
associated benefits, geographic information
system technology is being installed at
USGS regional centers for operational use
in mineral-resource-related and allied geo-
science investigations.

Advancements In Data

By Stephen C. Guptill

The keystone of making maps with computers lies in having a set of information in the computer that represents, in a digital form, the information to be shown

Table 1. Example information for the map shown in figure 10

Areas ID# Attribute 1 Map exterior 2 Park 3 Null 4 Null 5 Null 6 Lake 7 Null Lines Node Area ID# Attribute Coordinates Start End Left Right 1 Map border 5,65...... 25,65 1 2 1 2 2 Map border 25,65.....35,65 2 3 1 3 3 Map border 35,65.....65,65 3 4 1 5 4 Map border 65,65.....65,53 4 5 1 5 5 Map border 65,53.....65,5 5 6 1 7 6 Map border 65,5...... 55,5 6 7 1 7 7 Map border 55,5...... 5,5 7 8 1 4 8 Map border 5,5........ 5,23 8 9 1 4 9 Map border 5,23...... 5,45 9 10 1 3 10 Map border 5,45...... 5,65 10 1 1 2 11 Boundary 25,65.....5,45 2 10 3 2 12 River 35,65.....38,40 3 11 5 3 13 Road 5,23...... 38,40 9 11 3 4 14 Road 38,40.....65,53 11 5 5 7 15 River 38,40..... 55,5 11 7 7 4 16 Shoreline 35,55.....35,55 14 14 7 6 Nodes ID# Attribute Coordinates 1 Null 5,65 2 Null 25,65 3 Null 35,65 4 Null 65,65 5 Null 65,53 6 Null 65,5 7 Null 55,5 8 Null 5,5 9 Null 5,23 10 Null 5,45 11 River 38,40 12 House 28,28 13 House 33,31 14 Null 35,55

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