DESCRIPTIVE MODELS

Descriptive models are of many formats but in general have m common the name of deposit type or some other identifier, commonly related to classifications of mineral deposits, and have various elements of the geological environment, such as rock types and textures, age of mineralization, and tectonic setting, and the deposit description, including its geochemistry,

geophysical characteristics, geometry, ore controls, zoning patterns, and other features (Singer and Cox, 1988). Descriptive models of four types of uranium deposits are in the "Deposit model book" (Cox and Singer, 1986), and others less formalized are scattered in other publications (Mathews and others, 1979; Otton, 1987). The principal uses of descriptive models are to identify possible deposit types for a given environment, to evaluate the favorable portions of an area being assessed, to evaluate the degree of favorability of permissive portions, and to help estimate parameters for the various assessment methods, such as expected number of deposits in an area for the size-classes in the DSF method and for the MARKS simulation process.

COMPARISON OF RESOURCE ESTIMATES OBTAINED USING THE DSF AND MARK3 METHODS

The DSF method is a modification of the standard MURE estimation equation, U= A-F-T-G , (A=area, F=fraction of area mineralized, T=tons per unit area, G=grade) by replacing the factors PT by a single factor that represents the tonnage of the total number of deposits in all size classes.

Use of the DSF method requires knowledge of the size frequency of deposits and distribution of average grade in a well- but not completely- explored control area (table 1). The favorable area, A, is measured in appropriate units. In the DSF equation, a likelihood factor, L (generally 1 or less than 1), is used to express the similarity of the favorable area to the control area. The probability distribution estimates of undiscovered uranium endowment are calculated by entering the required data into the DSF equation (see Finch and McCammon, 1987) and by using the TENDOWG computer program (McCammon and others, 1988), a modification of the program by Ford and McLaren, (1980).

The MARKS method uses a Monte Carlo simulation process to calculate the undiscovered uranium ore estimates in the form of percentiles (for example, the 90th, 50th, 10th) based on

1 The MURE equation was developed because typical sandstone-type uranium deposits cannot be defined well enough to be individually counted. A cluster concept has been proposed to allow the DSF methods to be used for these sandstone deposits (Finch, 1991; Finch, Grundy, and Pierson, in press).

subjective estimates of the probable number of deposits in the region being assessed and on the statistics used to construct the appropriate grade and tonnage models (Drew and others, 1986; Reed and others, 1989; Root and Scott, 1988). For the area being assessed, the estimates of the number of deposits is judged partly on the size of the area but more importantly on the geology of the area compared to that of known districts and on exploration history. Attention is a paid to the abundance of structures and other features known to localize the deposits.

A comparison of the results obtained using the DSF method with those obtained using the MARK3 method for solution-collapse breccia pipe uranium deposits in the Grand Canyon region in Arizona is shown in figure 5. The mean value for the undiscovered uranium endowment for the DSF method is 14,905 (about 15,000) tonnes UsOg (Finch and others, 1990), and mean value of undiscovered uranium ore for the MARKS method is 11,666 (about 12,000) tonnes UsOg. The DSF result includes smaller deposits than the MARKS method and considering these basic differences in the kind of material estimated by the two methods, the results are remarkably similar.

COMPARISON OF RESOURCE ESTIMATES DSF METHOD VERSUS MARK3 METHOD

10 15 20 25 TONNES U3O8

(Thousands)

30 35

Figure 5. Graph showing the probability estimates of unconditional uranium resources based on the deposit-size-frequency and MARKS methods

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USE OF DOE GRADE-TONNAGE CURVES TO ESTIMATE POTENTIAL URANIUM RESOURCES

In the past, DOE has applied economic factors to the uranium endowment estimate to obtain the potential (mineable) resources in three forward-cost categories (Ford and McClaren, 1980;

Blanchfield, 1980; Das and Lee, 1991). The grade-tonnage curves are used to define a probability distribution for grade that is used to calculate the probability distribution of tonnage of

undiscovered resources. The three parameters from the grade-tonnage curve are (1) the average grade of the endowment (grade cutoff=0.01 percent UsOg), (2) slope of the "average grade of inventory" Une (generally the linear segment above 0.04 percent cutoff grade), and (3) average grade of ore at a cutoff grade of 0.04 percent UsOg (see Appendix B, p. 75 of Blanchfield, 1980 for details). These are combined with various physical and economic market factors in the DOE URAD [Das and Lee, in press; not to be confused with IAEA URAD (Uranium Reserves and Data) in IAEA-TECDOC-484] to calculate the estimated potential uranium resources for each of $30,

$50, and $100 per pound UsOg cost categories in each favorable area. Summaries of the potential resources for the U.S. are published each year by Energy Information Administration of DOE (Energy Information Administration, 1990).

USE OF STANDARD USGS GRADE AND TONNAGE MODELS TO PLAN AND EVALUATE EXPLORATION

The standard USGS grade and tonnage models coupled with descriptive models are useful to plan and execute exploration. Pre-exploration study can be guided by these models. In a frontier area, the models for a given type deposit are an indication of the target size, and one can plan a drilling program that takes into account the expected sizes of undiscovered uranium deposits. Once a discovery has been made and size of the deposit determined, one can plot its grade and tonnage on an appropriate model graph to give some idea about probable grade and tonnage of the remaining undiscovered deposits. For example, if the geology indicates that unconformity-related vein deposits can be expected, the model to use is shown in figure 1. In mature areas, plotting of the distributions of grades and tonnages of known deposits and

comparing the plots with available models provides an insight into possible grades and tonnages of undiscovered deposits.

Grade and tonnage models offer a way to compare distributions between deposit types, and thus have a bearing on management decisions to explore one type over another type. Relations of grade and tonnage among the three types of deposits modelled in figures 1-3 are shown in figures 6 and 7. These figures show significant differences: grades for unconformity-related deposits have a much wider range than those for volcanogenic deposits, and breccia pipe deposits have a narrower range of grade at about the midpoint of the grade for unconformity-related deposits. The sizes of the three types are grouped more tightly.

0.032 0.056 0.1 0.18 0.32 0.56 1.0 1.8 3.2 5.6 10.0 GRADE IN PERCENT U³O⁸

Figure 6. Comparison of grade models for unconformity-related, volcanogenic, and breccia-pipe uranium deposits

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O)

00004 OMIS 00063 0025 01 04 16 6J 25 100 100 MILLION TONNES ORE

Figure 7 Comparison of tonnage models for unconformity-related, volcanogemc, and breccia-pipe uranium deposits

UNCONFORMITY (WORLD)

0056 01 018 032 056 10 1.8 3.2 S 6 100 GRADE m PERCENT

Figure 8 Grade models for unconformity-related uranium deposits for Canada (lower oasnea une;

and Australia (upper dashed line) ( from Ru2acka, 1990)

UNCONFORMITY (AUSTRALIA)

00016 000630025 01 04 16 63 25 MILLION TONNES

100 400 1 600

Figure 9 Tonnage model for Australian unconformity-related deposits in solid line, lower dashed line for deposits hosted by metasedimentary rocks adjacent to major Archean/Proterozoic granite complexes, middle dashed line for deposits associated with metasedimemary rocks near to smaller to distant from large gramnc complexes, upper dashed line for small deposits associated with volcano-sedimentary rocks (from Ruzicfca, 1990)