IntroductionIT IS DIFFICULT to estimate the Earth’s undiscovered mineralresources. Our best information about undiscovered re-sources comes from past exploration successes and failures.This history provides models of what to expect in the future.For example, most discovered copper, similar to other metals,is located in the few largest deposits (Singer, 1995), and weexpect the same pattern to hold in the future. Of fundamen-tal concern in considering the future supply of copper orother metals is estimation of the numbers and locations of anylarge deposits that remain to be found. For undiscovered re-sources, mineral deposit types and their models are the bestknown predictors of deposit size. Over 50 percent of knowncopper resources are in porphyry copper-type deposits(Singer, 1995). Improved procedures to estimate possible lo-cations, sizes and grades, and number of undiscovered por-phyry copper deposits would therefore significantly advanceour ability to estimate undiscovered copper resources.
A primary function of many forms of quantitative mineralresource assessments is estimation of the number of undis-covered deposits. In any given region, some fixed, but in mostcases unknown, number of undiscovered deposits of a giventype is present. The number could be zero or a larger integer.Many quantitative mineral resource assessments based on acommon form of assessment (Singer, 1993) have used subjec-tive methods to estimate the number of deposits. Estimates ofthis unknown number are presented in a probabilistic form toreflect the uncertainty associated with the estimate.
Ideally, estimates of the number of deposits should rely onanalogies with similar well-explored areas, just as grades andtonnages of well-explored deposits serve as analogs of thegrades and tonnages of undiscovered deposits. Estimates canbe derived from counts of known deposits per unit area in ex-plored regions. The number of deposits per unit area can beillustrated in histograms that show variation of densities bydeposit type. Some research has been conducted on densitiesof several deposit types so that these ratios can be morewidely used as a guide for estimating numbers of deposits
Porphyry Copper Deposit Density
DONALD A. SINGER,† VLADIMIR I. BERGER, U.S. Geological Survey, 345 Middlefield Road, Menlo Park, California 94025
W. DAVID MENZIE, U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston, Virginia 20192
AND BYRON R. BERGER
U.S. Geological Survey, Mail Stop 964, Box 25046, Denver, Colorado 80225
AbstractEstimating numbers of undiscovered mineral deposits has been a source of unease among economic geolo-
gists yet is a fundamental task in considering future supplies of resources. Estimates can be based on frequen-cies of deposits per unit of permissive area in control areas around the world in the same way that grade andtonnage frequencies are models of sizes and qualities of undiscovered deposits. To prevent biased estimates itis critical that, for a particular deposit type, these deposit density models be internally consistent with descrip-tive and grade and tonnage models of the same type. In this analysis only deposits and prospects that are likelyto be included in future grade and tonnage models are employed, and deposits that have mineralization or al-teration separated by less than an arbitrary but consistent distance—2 km for porphyry copper deposits—arecombined into one deposit. Only 286 deposits and prospects that have more than half of the deposit not cov-ered by postmineral rocks, sediments, or ice were counted.
Nineteen control areas were selected and outlined along borders of hosting magmatic arc terranes based onthree main features: (1) extensive exploration for porphyry copper deposits, (2) definable geologic settings ofthe porphyry copper deposits in island and continental volcanic-arc subduction-boundary zones, and (3) diver-sity of epochs of porphyry copper deposit formation.
Porphyry copper deposit densities vary from 2 to 128 deposits per 100,000 km2 of exposed permissive rock,and the density histogram is skewed to high values. Ninety percent of the control areas have densities of fouror more deposits, 50 percent have densities of 15 or more deposits, and 10 percent have densities of 35 or moredeposits per 100,000 km2. Deposit density is not related to age or depth of emplacement. Porphyry copper de-posit density is inversely related to the exposed area of permissive rock. The linear regression line and confi-dence limits constructed with the 19 control areas can be used to estimate the number of undiscovered de-posits, given the size of a permissive area. In an example of the use of the equations, we estimate a 90 percentchance of at least four, a 50 percent chance of at least 11, and a 10 percent chance of at least 34 undiscoveredporphyry copper deposits in the exposed parts of the Andean belt of Antarctica, which has no known depositsin a permissive area of about 76,000 km2. Measures of densities of deposits presented here allow rather simpleyet robust estimation of the number of undiscovered porphyry copper deposits in exposed or covered permis-sive terranes.
(Bliss et al., 1987; Bliss, 1992; Root et al., 1992; Bliss andMenzie, 1993). Most of these studies provide point (that issingle) estimates of the number of deposits per unit area.Singer et al. (2001) summarized the ideas behind these min-eral deposit density models and provided individual estimatesfor 27 combinations of deposit types and locations. Many ofthe selected areas described provide standards to identifywhat should be considered high estimates of the number ofundiscovered deposits in most situations. Thus, most pub-lished mineral deposit densities provide possible upper limitsto estimates and are not necessarily representative of morecommon situations. Little published information is availableconcerning the variability of deposit densities within deposittypes. In this study, we address the issues related to variabil-ity of deposit densities within the porphyry copper deposittype along with questions about effects of map or assessmentscale on estimates of densities.
Nineteen porphyry copper belts or control areas fromaround the world were selected for this study, and depositdensities were estimated (Fig. 1, Table 1). In general, depositdensity is the number of porphyry copper deposits in a mag-matic arc divided by the area of the arc. The selected areasare described after discussions on the basic definitions of por-phyry copper subtypes, delineation of mineral belts, depositages, and scales of geologic maps used. Information on indi-vidual deposits and prospects is provided in a recently up-dated database of porphyry copper deposits of the world(Singer et al., 2005). Three control areas in the Philippinesand four control areas in South America serve as examples ofthe approach and illustrate some of the delineation issues.Next, effects on the estimates of cover over deposits are dis-cussed. Preliminary estimates of deposit density are then pre-sented. Possible effects of deposit ages, scales of maps used,and extent of exploration in an area are addressed, and an ex-ample of the use of deposit densities in Antarctica is provided.Information about the 19 porphyry copper control areas andsources of information used for delineation of the belts areprovided in an Appendix.
Estimation of Deposit DensityIn order to determine the usefulness of porphyry copper
deposit densities, underlying assumptions and operationalrules for constructing the models need to be considered. De-posit density models must be used within an internally con-sistent assessment system and this affects how the modelsshould be constructed. For the density model presented here,tectonic settings that contain porphyry copper deposits formthe underlying basis for delineation of control areas and per-missive areas in the application of the model. Rules for thedelineation of control areas follow with some examples todemonstrate the principles and illustrate where caution is re-quired.
Some assumptions and standards
Care must be exercised in quantitative assessments of undis-covered resources to prevent the introduction of biased esti-mates. This requires an internally consistent set of models andapplications. In a three-part quantitative assessment, (1) areasare delineated according to types of deposits permitted by thegeology, (2) amounts of metals and some ore characteristics
are estimated by means of grade and tonnage models, and (3)the number of undiscovered deposits of each type within thepermissive areas is estimated (Singer, 1993). Permissive areasare delineated where geology allows the existence of depositsof one or more specified types. These areas are based on ge-ologic criteria derived from deposit models that are them-selves based on studies of known deposits within and, morecommonly, outside the study area. Permissive boundaries aredefined such that the probability of deposits of the type de-lineated occurring outside the boundary is negligible (Singeret al., 2001).
A critical part of exploration for mineral deposits and ofquantitative mineral-resource assessments is the estimationof the sizes of undiscovered deposits. Typically, this problemis addressed by using grade and tonnage models for a partic-ular deposit type (Singer and Kouda, 1999). In three-part as-sessments, a previously constructed grade and tonnage modeltypically is used, unless local deposits in the study area are sig-nificantly different from those in the general model. Thesemodels have the form of frequency distributions of tonnagesand average grades of well-explored deposits of each type.They serve as models for grades and tonnages of undiscov-ered deposits of the same type occurring in geologically simi-lar settings. Therefore, when the number of undiscovered de-posits is estimated using a deposit density model or any othermethod, the estimates must be consistent with the grade andtonnage model and the rules used to construct that model.For construction of grade and tonnage models, depositsknown to be only partially drilled are counted as prospectsand not used in order to avoid introduction of biases. Phrasessuch as “open on the west” or “more drilling is planned” areused to classify the deposits as prospects for the grade andtonnage models. In contrast, during construction of densitymodels, prospects that are likely to be included in futuregrade and tonnage models are counted as deposits.
The third part of three-part assessments is the estimate ofthe fixed, but unknown, number of deposits of each type thatexist in the delineated tracts. Until the area being consideredis thoroughly and extensively drilled, that fixed number ofundiscovered deposits, which could be almost any number in-cluding zero, will not be known with certainty. In three-partassessments, estimates of the number of deposits explicitlyrepresent the probability (or degree of belief) that some fixedbut unknown number of undiscovered deposits exists in thedelineated tracts. As such, these estimates reflect both theuncertainty of what may exist and a measure of the favorabil-ity of the existence of the deposit type. Uncertainty is indi-cated by the spread of the estimated number of deposits as-sociated with the 90th to the 10th or 1st percentiles—a largerelative difference suggests great uncertainty. Favorablenessis represented by the estimated number of deposits associ-ated with a given probability level or by the expected (i.e.,mean) number of deposits.
One form of mineral deposit model that can be constructedis based on counts of numbers of deposits per unit area inwell-explored regions. The resulting frequency distribution isused either directly for an estimate or indirectly as a guidelinein some other method. Histograms of the number of depositsper area show how commonly different deposit densities arepresent. It is relatively easy to determine if an area is well
492 SINGER ET AL.
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PORPHYRY COPPER DEPOSIT DENSITY 493
0361-0128/98/000/000-00 $6.00 493
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494 SINGER ET AL.
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explored for some deposit types, such as porphyry copper orplacer gold, if the deposits are exposed and the area is notheavily vegetated. Because of the difficulty of recognizingother types of deposits, such as sediment-hosted copper, de-termining the extent and efficiency of exploration in an areais more difficult. It is not necessary that the control areas areexplored completely, but it is necessary that the number ofdeposits found and the proportion of the area explored be es-timated.
The assessments are internally consistent when (1) the de-lineated tracts are consistent with descriptive models, (2)grade and tonnage models are consistent with descriptivemodels, (3) grade and tonnage models are consistent withknown deposits in the area, and (4) estimates of the numberof deposits are consistent with the grade and tonnage mod-els—that is, about one half of the estimated deposits havetonnages greater than the median tonnage in the grade andtonnage model. Biases can be introduced into these estimatesby a grade and tonnage model that is flawed by including de-posits that are from more than one type or that are incom-pletely explored. Biases also can be introduced by lack of con-sistency of the estimated number of deposits with the gradeand tonnage model.
Definition of porphyry copper deposits
Determining a mineral deposit density requires unambigu-ous definitions of what is a deposit and what are the rules fordelineation. A mineral deposit is defined as a mineral occur-rence of sufficient size and grade that might, under the mostfavorable circumstances, be considered to have economic po-tential (Cox et al., 1986). According to Cox (1986), porphyrycopper deposits consist of stockwork, disseminated, and brec-cia-hosted copper mineralization together with K silicate al-teration that is generally restricted to porphyritic stocks andtheir immediate wall rocks. The deposits may have parts thatcontain skarn. Deposits that may be derived from, or affectedby, supergene processes are included in the models. An im-portant consideration at the data gathering stage and, as aconsequence, at the assessment stage is the question of whatthe sampling unit should be. Grade and tonnage data areavailable to varying degrees for districts, deposits, mines, andshafts. For the deposits used in this study (Singer et al., 2005)the following operational rule was consistently applied to de-termine which orebodies were combined. All mineralizedrock or alteration separated by less than 2 km of unmineral-ized or unaltered rock was combined into one deposit. Thus,if the alteration zones of two deposits are within 2 km of eachother, the deposits were combined. In resource assessmentssuch an operational spatial rule is necessary for defining de-posits because we must be able to classify deposits in regionswith highly variable geologic information and to avoid bias inestimating undiscovered deposits in areas where detailed in-formation is lacking, such as under cover. The 2-km rule is ap-plied so that deposits in grade and tonnage and spatial densitymodels correspond as closely as possible to deposits as geo-logic entities. Any spatial rule can lead to one of two errors.For example, postmineral faulting can result in one geologicdeposit being dismembered into two or more spatially distinctparts (e.g., Ely, Nevada). Alternatively, temporally distinct de-posits can be emplaced in close proximity to each other.
Other complexities may be introduced as the result of legalownership. The 2-km rule was chosen to minimize errors ofboth types. Rules such as the 2-km rule used here are essen-tial in order to have an internally consistent assessment sys-tem where the estimate of number of undiscovered depositsis consistent with the grade and tonnage model.
Porphyry copper deposits, as defined for these models,have been divided into three subtypes: copper (Cu-Au-Mo),copper-gold (Cu-Au), and copper-molybdenum (Cu-Mo),which are discriminated by a ratio of Au(g/t)/Mo (%) that is≥30 in the Cu-Au subtype, and ≤3 in the Cu-Mo subtype (Coxand Singer 1986, 1992). These subtypes were developedbased upon some of the data and patterns originally pre-sented in Brown (1976). Gold-rich porphyry copper deposits(Cu-Au) tend to have been emplaced at a shallower depththan molybdenum-rich (Cu-Mo) deposits, with the generalsubtype (Cu-Au-Mo) formed at intermediate depths (Table2). All three subtypes might be present in the same generalgeologic setting, but the likelihood of occurrence could bedifferent depending on local or regional geologic conditions,such as depth of erosion.
Delineation of control areas
Delineation of areas permissive for porphyry copper de-posits requires knowledge of the tectonic settings of the de-posits. Island and continental volcanic-arc subduction-bound-ary zones are where porphyry copper deposits are formed(Sillitoe, 1972), and the nature of arcs can affect the timingand spatial distribution of porphyry copper deposits. Arcmagmas are derived from partial melting of the mantle wedgein the overriding plate owing to the upward migration of de-hydration fluids from the subducting slab. Dehydration reac-tions in subducting slabs are pressure and temperature de-pendent and, therefore, occur over regular depth intervals tothe Wadati-Benioff zone (e.g., Anderson et al., 1978; Tatsumiand Eggins, 1995; Julian, 2002). On the trench side of arcs,the thermal structure of subduction zones results in a definiteline of volcanoes, the “volcanic front.” Widths of arcs, from thevolcanic front to the back-arc, vary considerably, for examplefrom ≈10 km in part of the Mariana arc to ≈200 km in part ofthe Andes (Tatsumi and Eggins, 1995). The width of arcs isinversely proportional to the steepness of the subductionangle (Marsh, 1979), a reflection of the pressure dependenceof dehydration reactions. Wide arcs where the subduction
PORPHYRY COPPER DEPOSIT DENSITY 495
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TABLE 2. Some Features of the Three Subtypes of Porphyry Copper Deposits1,2
1 All values except number of deposits are medians2 Information sources: Grades and tonnages, Singer et al. (2005); mag-
netite content and interpreted depths of emplacement, Cox and Singer(1992)
angle is low may consist of two or even three distinct trench-parallel volcanic chains (e.g., Sunda and Kamchatka arcs) orhave a very uneven distribution of volcanoes behind the vol-canic front (e.g., Kurile arc) (Tatsumi and Eggins, 1995).Even where volcanoes are irregularly distributed, however,the greatest volume of volcanic rocks is erupted along the vol-canic front (e.g., De Silva and Francis, 1991).
Gaps in space and in time in volcanic activity characterizemany subduction zones. These discontinuities reflect varyingdip angles of the subducting slab (Chen et al., 2001). Alongthe volcanically active parts of subduction zones, the arc iscommonly segmented (e.g., Central America and Aleutianarcs) such that some groups of volcanoes are offset from othergroups. Segment boundaries can localize volcanoes alonglines transverse to the trench (e.g., Macolod corridor, Luzon,Philippines). Segmentation can be a reflection of a seg-mented subducting slab (Tatsumi and Eggins, 1995) or bedue to transverse fault zones wholly within the overridingplate.
In some arcs the volcanoes are approximately evenly spaced(e.g., 78 ± 32 km in the Cascade, Aleutian, and AlaskanPeninsula arcs; Marsh and Carmichael, 1974). In other arcs,there is considerable variation (e.g., 72 ± 72 km in the NewBritain arc; 32 ± 23 km in the Central America arc: Tatsumiand Eggins, 1995). Overall, Tatsumi and Eggins (1995) indi-cated that volcano spacing is too irregular to be of predictiveimportance, although the linear density of volcanoes (n/100km) shows a positive relationship to the rate of convergence(Shimozuru and Kubo, 1983). This relationship implies thatfaster rates of subduction result in higher melt production inthe mantle wedge (Tatsumi and Eggins, 1995).
Nakamura and Uyeda (1980) described the stress fields inseveral subduction-boundary zones using dike swarms, faults,and earthquake-source mechanisms. They found the tectonicstress field has a gradient in its horizontal component (σH)while the vertical component (σV) remains constant at thesame depth and equal to the vertical load. In the arcs studied,thrust faulting occurred in the fore-arc region, strike-slipfaulting in the arc, and normal faulting in the back-arc region.
Porphyry copper deposits are present in narrow (e.g.,northern Philippines and northern Peru) as well as broad vol-canic arcs (e.g., eastern Chile and western Argentina), and atmore locations than just along the volcanic front. For exam-ple, in the late Miocene to Pleistocene western Luzon arc,Philippines, roughly a dozen deposits are present in a narrowline along the volcanic front and four deposits are presentalong a second volcanic chain 30 km farther east (e.g., Geo-logical Survey Division of Philippines, 1963; Sillitoe andGappe, 1984). In Chile, deposits formed at the volcanic front(e.g., El Teniente; cf. Sillitoe, 1988) coevally with depositsthat formed in a second volcanic chain (e.g., Bajo de la Alum-brera; cf. Sasso and Clark, 1998), approximately 300 km far-ther east, although, as in the Philippines, more porphyry-typedeposits are present at the more voluminous volcanic front.
Porphyry copper deposits are localized by strike-slip andrelated faults within volcano-plutonic arc complexes. In acompilation of the structural geology of porphyry-style de-posits worldwide, Berger and Drew (2002) found them tooccur most frequently in releasing bends into extensionalstepovers along strike-slip systems (e.g., Grasberg, Indonesia;
cf. Sapiie and Cloos, 2004). Less frequently, they occur in in-line grabens along strike-slip faults (e.g., Silver Bell, Arizona;Sawyer, 1996) and in arrays of extension-accommodatingfaults that branch away from a master strike-slip fault in themanner of fans or horsetails (e.g., Chuquicamata, Chile;Lopez, 1942; Lindsay, 1997).
Broad volcanic arcs that formed at approximately the sametime are the fundamental unit for the delineation of permis-sive control areas for porphyry copper deposits used here.Much of the information for the present report is from the re-cently revised database of porphyry copper deposits of theworld (Singer et al., 2005) and various geologic maps and ad-ditional materials. For 19 control areas selected from aroundthe world, permissive areas were delineated, measured, anddeposit densities were estimated. Bliss and Menzie (1993)discuss general rules for delineating control areas and theirrelationship to deposit definitions. In this study the controlareas were selected and delineated on the basis of three maincriteria: (1) extensive exploration for porphyry copper de-posits, (2) a definable geologic setting of the porphyry copperdeposits in various tectonic environments, and (3) a diversityof epochs of porphyry copper deposit formation. The lattertwo features provide the variety of information needed to ad-dress issues in subsequent analysis. We do not attempt to in-clude all of the world’s porphyry copper belts because ourpurpose is to construct and test a model of the density of por-phyry copper deposits. The general locations and ages of theporphyry copper control areas are shown in Table 1 and theirdistribution is shown in Figure 1. The 19 control areas con-tain about 75 percent of the total amount of copper in allknown porphyry copper deposits. In the present study, bothporphyry copper deposits in existing grade and tonnage mod-els and porphyry copper prospects that we believe, uponthorough exploration drilling, will eventually be included ingrade and tonnage models are counted. Prospects arecounted separately here because, in any subsequent resourceassessment, only deposits represented by the grade and ton-nage models are counted as discovered deposits. Depositsand prospects used in this study are identified in Table 3.Other deposits may be included in this list, but, as discussedlater, this does not substantially change the results of themodel.
The control areas used in this study are outlined along bor-ders of magmatic arcs, as discussed above, taking into consid-eration the deposit ages and distributions of major structures.Locations of the control areas are approximately indicated bybordering coordinates provided in Table 1. The control areasare delineated on the basis of information in geologic mapsranging in scale from 1:240,000 to 1:2,500,000 (Table 1). Themaps typically were published in the 1980s with a few fromthe 1990s. An important region of South America at the con-junction of Argentina, Bolivia, Chile, and Peru is covered bycompiled geologic and metallogenic maps of 1:1,000,000scale (Zappettini et al., 2001). Information about delineationand map sources for each of the 19 control areas is providedin the Appendix.
Examples of control areas
Some control areas delineated as permissive for porphyrycopper deposits in the Philippines and in South America are
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TABLE 3. Porphyry Copper Deposits, Ages, Subtypes, Percent Cover, and Control Areas (prospects listed in italics)
Deposit Age Subtype Cover (%) Control area
Batong Buhay Late Miocene Cu-Au nd (1) Western PhilippinesBoneng Lobo 10.5 Cu-Au 0 (1) Western PhilippinesBotilao ? Cu-Au nd (1) Western PhilippinesDizon 2.7 Cu-Au 0 (1) Western PhilippinesFar Southeast-Bato Tabio 1.4 Cu-Au 90 (1) Western PhilippinesGuinaoang-Tirad 3.5 Cu-Au 0 (1) Western PhilippinesHale-Mayabo ? Cu-Au nd (1) Western PhilippinesKennon SouthEast 2 Cu-Au-Mo 0 (1) Western PhilippinesKilongalao ? Cu-Au-Mo nd (1) Western PhilippinesManag ? Cu-Au nd (1) Western PhilippinesPisumpan 4.1–0.19 Cu-Au 0 (1) Western PhilippinesSan Antonio-Philex ? Cu-Au 0 (1) Western PhilippinesSanto Niño 9.5 Cu-Au-Mo 0 (1) Western PhilippinesSanto Tomas II 1.5 Cu-Au 0 (1) Western PhilippinesTawi-Tawi 5.6–3.5 Cu-Au-Mo 0 (1) Western PhilippinesTaysan 7.3 Cu-Au 70 (1) Western PhilippinesSinipsip ? Cu-Au-Mo nd (1) Western PhilippinesAya-Aya Pre-middle Miocene Cu-Au-Mo 0 (2) Central PhilippinesBalak-5 ? Cu-Au-Mo nd (2) Central PhilippinesBasay Oligocene Cu-Au 0 (2) Central PhilippinesDinkidi 23 Cu-Au 0 (2) Central PhilippinesHinobaan 20–15 Cu-Au 0 (2) Central PhilippinesIno-Capayang ? Cu-Au-Mo 0 (2) Central PhilippinesLumbay ? Cu-Au 0 (2) Central PhilippinesMarcopper 21–20 Cu-Au 0 (2) Central PhilippinesMarian 25 Cu-Au 0 (2) Central PhilippinesMatanlang Early Miocene Cu-Au 0 (2) Central PhilippinesSan Fabian 21 Cu-Au 0 (2) Central PhilippinesSipalay 30 Cu-Au-Mo 0 (2) Central PhilippinesSuguibon ? Cu-Au-Mo 0 (2) Central PhilippinesLabangan ? Cu-Au-Mo nd (2) Central PhilippinesSalatan Pre-middle Miocene Cu-Au-Mo nd (2) Central PhilippinesAmacan Middle-late Miocene Cu-Au 0 (3) Eastern PhilippinesKalamatan ? Cu-Au nd (3) Eastern PhilippinesKingking Middle-late Miocene Cu-Au 0 (3) Eastern PhilippinesLuna-Asiga Pliocene Cu-Au-Mo 0 (3) Eastern PhilippinesMapula Middle-late Miocene Cu-Au 0 (3) Eastern PhilippinesSulat Middle-late Miocene Cu-Au 0 (3) Eastern PhilippinesTagruga Maangob ? Cu-Au-Mo 0 (3) Eastern PhilippinesTampakan Pliocene Cu-Au 0 (3) Eastern PhilippinesBoyongan 3.5 Cu-Au-Mo nd (3) Eastern PhilippinesTapadaa 5-2.5 Cu-Au 0 (4) Northern Sulawesi, IndonesiaTombulilato 3 Cu-Au 10 (4) Northern Sulawesi, IndonesiaBulagidun 8.75 Cu-Au 0 (4) Northern Sulawesi,Indonesia Afton 206 Cu-Au 90 (5) Quesnellia-BC, Canada, and WA, United StatesAjax 206 Cu-Au 90 (5) Quesnellia-BC, Canada, and WA, United StatesAxe Jurassic Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesBetlehem 200 Cu-Mo 90 (5) Quesnellia-BC, Canada, and WA, United StatesBrenda 143 Cu-Mo 0 (5) Quesnellia-BC, Canada, and WA, United StatesBronson 195 Cu-Au nd (5) Quesnellia-BC, Canada, and WA, United StatesCopper Canyon 212 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United StatesCopper Mountain 198 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United StatesEaglehead 186 Cu-Au-Mo 0 (5) Quesnellia-BC, Canada, and WA, United StatesGalaxy ? Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesGalore Creek 208 Cu-Au 90 (5) Quesnellia-BC, Canada, and WA, United StatesGibraltar 217–203 Cu-Au-Mo 70 (5) Quesnellia-BC, Canada, and WA, United StatesGnat Lake ? Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesHighmont ? Cu-Mo 0 (5) Quesnellia-BC, Canada, and WA, United StatesKelsey Jurassic Cu-Au nd (5) Quesnellia-BC, Canada, and WA, United StatesKemess North 202 Cu-Au 80 (5) Quesnellia-BC, Canada, and WA, United StatesKemess South 202 Cu-Au 80 (5) Quesnellia-BC, Canada, and WA, United StatesKerr 194 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United StatesKrain ? Cu-Au-Mo 50 (5) Quesnellia-BC, Canada, and WA, United StatesKwanika 121 Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesLexington-Lone Star 200 Cu-Au 90 (5) Quesnellia-BC, Canada, and WA, United StatesLornex 202–192 Cu-Mo 80 (5) Quesnellia-BC, Canada, and WA, United StatesLorraine 182–162 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United StatesMt. Milligan 188–183 Cu-Au 90 (5) Quesnellia-BC, Canada, and WA, United StatesMt. Polly 202 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United States
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Pine Jurassic? Cu-Au 40 (5) Quesnellia-BC, Canada, and WA, United StatesRed Bluff 195 Cu-Au 0 (5) Quesnellia-BC, Canada, and WA, United StatesRed Chris 210–195 Cu-Au 20 (5) Quesnellia-BC, Canada, and WA, United StatesSchaft Creek 220 Cu-Au-Mo 70 (5) Quesnellia-BC, Canada, and WA, United StatesSulphurets 194 Cu-Au 20 (5) Quesnellia-BC, Canada, and WA, United StatesValley 202–190 Cu-Mo 90 (5) Quesnellia-BC, Canada, and WA, United StatesChuchi 181 Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesKatie ? Cu-Au nd (5) Quesnellia-BC, Canada, and WA, United StatesMisty 175.5 Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesRayfield ? Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesTrojan ? Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesWhipsaw ? Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesWilla 183 Cu-Au-Mo nd (5) Quesnellia-BC, Canada, and WA, United StatesBell Copper 53 Cu-Au 60 (6) Stikinia BC, Canada, and WA, United StatesBerg 52–47 Cu-Mo 0 (6) Stikinia BC, Canada, and WA, United StatesBig Onion 56–49 Cu-Au-Mo 70 (6) Stikinia BC, Canada, and WA, United StatesDorothy 50 Cu-Au-Mo nd (6) Stikinia BC, Canada, and WA, United StatesFish Lake 79 Cu-Au 95 (6) Stikinia BC, Canada, and WA, United StatesGiant Copper ? Cu-Au-Mo nd (6) Stikinia BC, Canada, and WA, United StatesGranisle 51 Cu-Au-Mo 0 (6) Stikinia BC, Canada, and WA, United StatesHuckleberry 82 Cu-Mo 50 (6) Stikinia BC, Canada, and WA, United StatesLouise Lake ? Cu-Au-Mo 90 (6) Stikinia BC, Canada, and WA, United StatesMaggie 61 Cu-Mo 100 (6) Stikinia BC, Canada, and WA, United StatesMazama 90–87 Cu-Au-Mo 50 (6) Stikinia BC, Canada, and WA, United StatesMorrison 50 Cu-Au-Mo 0 (6) Stikinia BC, Canada, and WA, United StatesOx Lake 84 Cu-Au-Mo 20 (6) Stikinia BC, Canada, and WA, United StatesPoison Mountain 58–55 Cu-Au-Mo 0 (6) Stikinia BC, Canada, and WA, United StatesPoplar 74 Cu-Au-Mo 80 (6) Stikinia BC, Canada, and WA, United StatesPrimar ? Cu-Au-Mo nd (6) Stikinia BC, Canada, and WA, United StatesTaseko 85 Cu-Au 50 (6) Stikinia BC, Canada, and WA, United StatesColes Creek 86 Cu-Au-Mo nd (6) Stikinia BC, Canada, and WA, United StatesLennak Lake 78 Cu-Au-Mo nd (6) Stikinia BC, Canada, and WA, United StatesWhiting Creek 84–81 Cu-Mo nd (6) Stikinia BC, Canada, and WA, United StatesAjo 63 Cu-Au-Mo 0 (7) Arizona-north Mexico Bagdad 71 Cu-Mo 0 (7) Arizona-north Mexico Cananea 58–53 Cu-Mo 0 (7) Arizona-north Mexico Casa Grande West 71–65 Cu-Mo 100 (7) Arizona-north Mexico Castle Dome 59 Cu-Au-Mo 0 (7) Arizona-north Mexico Chilito ? Cu-Au-Mo 0 (7) Arizona-north Mexico Christmas 63.5 Cu-Au-Mo 0 (7) Arizona-north Mexico Copper Basin 64 Cu-Au-Mo 0 (7) Arizona-north Mexico Copper Creek 58 Cu-Au-Mo 0 (7) Arizona-north Mexico Copper Flat 75 Cu-Au-Mo 60 (7) Arizona-north Mexico Cuatro Hermanos 58–55 Cu-Au-Mo nd (7) Arizona-north Mexico Cumobabi 63–40 Cu-Mo 0 (7) Arizona-north Mexico Esperanza-Sierrita 57–53 Cu-Mo 0 (7) Arizona-north Mexico Gibson 61 Cu-Au-Mo nd (7) Arizona-north Mexico Hannover Mountains 70–57 Cu-Au-Mo 0 (7) Arizona-north Mexico Helvetia 56-62 Cu-Au-Mo 0 (7) Arizona-north Mexico Ithaca Peak 73 Cu-Mo 10 (7) Arizona-north Mexico Johnson Camp 53 Cu-Au-Mo 0 (7) Arizona-north Mexico La Caridad 53–49 Cu-Au-Mo 0 (7) Arizona-north Mexico La Florida 52 Cu-Au-Mo 0 (7) Arizona-north Mexico La Reyna ? Cu-Au nd (7) Arizona-north Mexico Lakeshore 66 Cu-Au-Mo 80 (7) Arizona-north Mexico Lonesome Pine 61 Cu-Au-Mo nd (7) Arizona-north Mexico Mariquita ? Cu-Au-Mo nd (7) Arizona-north Mexico Miami-Inspiration 60 Cu-Mo 20 (7) Arizona-north Mexico Mineral Butte 70 Cu-Au-Mo 25 (7) Arizona-north Mexico Mipillas 58 Cu-Au-Mo nd (7) Arizona-north Mexico Mission–Pima 58 Cu-Au-Mo 0 (7) Arizona-north Mexico Morenci-Metcaff 56 Cu-Mo 15 (7) Arizona-north Mexico Nacozari 53 Cu-Au-Mo nd (7) Arizona-north Mexico Piedras Vedras ? Cu-Au-Mo 0 (7) Arizona-north Mexico Pine Flat ? Cu-Au-Mo 0 (7) Arizona-north Mexico Poston Butte 62 Cu-Au-Mo 100 (7) Arizona-north Mexico Ray 60.5 Cu-Mo 0 (7) Arizona-north Mexico Red Hills 70–60 Cu-Au-Mo nd (7) Arizona-north Mexico Red Mountain 62–58 Cu-Au-Mo 0 (7) Arizona-north Mexico
provided as examples in Figures 2 and 3. These two groups ofcontrol areas are used here to focus on some important issuesrelated to delineations.
Thirty-seven explored porphyry copper deposits and fourprospects are located on various islands of the Philippines
with a large proportion concentrated in northern Luzon (Fig.2). On the basis of ages of porphyry copper deposits, they canbe roughly divided into two groups: Oligocene to earlyMiocene and late Miocene to Pliocene. The Early CretaceousAtlas porphyry copper deposit (108 Ma) located on Cebu Is-
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120º
18º
16º
S. China
Basin Ridge
14º
12º
10º
6º
126º124º
124º
East
Luz
on T
roug
h
P h
i l
i p
p i
n e
T
r e
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8º
M a
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r e
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Early Miocene Tr
ench
Early M
iocene Magmatic
Arc
Neg
ros
Tre
nch
Early M
iocene Spreading Center
Catobato Trench
NORTH PALAVAN MICROCONTINENTAL BLOCK
LUZON
MINDORO
TABLAS
SIBUYAN
PANAY
CEBU
SAMAR
NEGROS
MINDANAO
BOHOLPALAWAN
100 50 0 100 200
Kilometers
WE
ST B
ELT
EA
ST B
ELTW
ES
T BE
LTC
EN
TRA
L
BE
LT
FIG. 2. Porphyry copper control areas in the Philippines. Western area: Late Miocene-Pleistocene, 10 to 1.2 Ma; centralarea: Oligocene-early Miocene, 30 to 17.5 Ma; East area: Late Miocene-Pliocene, 10 to 3.5 Ma. Black dots are porphyry cop-per deposits. The circled black dot is the Early Cretaceous Atlas porphyry copper deposit (108 Ma). Tectonic outlines basedon unpublished data of B.R. Berger.
land is an exception that cannot be assigned to any group, soit is not included in density calculations. The younger agegroup is located on both sides of the older, central Philippinecontrol area. The relationships between different island arcsin the Philippines are complex and variable in time and space(Ringenbach et al., 1990; Rangin, 1991; Hall, 2002). There-fore, these boundaries of the control areas should be consid-ered as approximate.
Control areas for porphyry copper deposits of differentages in the South American Andes (Fig. 3) were first outlinedby Sillitoe (1981, 1988, 1989). Similar areas in Chile wereshown by Camus and Dilles (2001) and Camus (2002). Re-cent information confirms Sillitoe’s approach except for somedetails. For instance, the age of the Taca Taca deposits innorthwestern Argentina was initially considered to be EarlyCarboniferous, but new K/Ar determinations (Rojas et al.,1999) and geologic data (Rubinstein et al., 1999; Zappettini etal., 1999; S. Blower, 2003, unpub. report for Lumina CopperCorp., http://www.luminacopper.com/s/TacaTacaProperty.asp)show that these deposits are related to 33.6 to 29 MaOligocene porphyry stocks. The control areas delineated inFigure 3 are modified from Sillitoe (1988). The three Ceno-zoic control areas young systematically eastward into the con-tinent from Paleocene to Pliocene, reflecting the eastwardmigration of arc volcanism (cf. Sillitoe, 1988; Farrar et al.,1970; Frutos, 1990; Espinoza et al., 1996; Zappettini et al.,2001). Fragments of a fourth area, including a few Late Pale-ozoic porphyry copper deposits, were delineated by Sillitoe(1977, 1988). However, our delineation of this fourth controlarea includes most of the Late Paleozoic magmatic arc alongthe western margin of South America as shown by Mpodozisand Ramos (1989) and Camus (2002).
Preliminary study indicated that the Peruvian Mioceneporphyry copper belt has two different deposit densities, so itwas divided into two parts: a northern control area, which ex-tends through Ecuador, and a central control area. This pre-vents the possibility of mixing two different densities withinthe same control area. Two older control areas extend fromnorthern Chile into southern Peru; the older control area isPaleocene and the younger is Eocene to Oligocene (Perelló etal., 2003). Newly discovered deposits in southeastern Ecuadornear Peru are part of a Late Jurassic porphyry copper beltthat is not considered because of the early stage of exploration(Gendall et al., 2000).
Effects of CoverBefore deposit density estimates are made, the area per-
missive for a deposit type should be adjusted for the portionthat is covered and therefore typically poorly explored. Ad-justed estimates for each control area are provided in Table 1.In most situations, covered parts of control areas cannot beconsidered to be well explored. Therefore, inclusion of cov-ered parts of such areas would increase the size of the controlareas and therefore decrease the deposit density. Further,mineral deposits that have been found under cover are simi-larly excluded because they would inflate density statisticsand lead to biased estimates of how many undiscovered de-posits might be present in an assessed permissive area.
Boundaries of mapped rock units form the primary basis fordrawing limits of permissive areas or control areas. Permissive
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OCEAN
PERU
BOLIVIA
ARGENTINA
CHILE
Paleocene
Eocene–Oligocene
Miocene–Pliocene
Permian–Triassic
75o 68o
75o 68o
15o
30o
37o30'
22o30'
15o
30o
37o30'
22o30'
FIG. 3. Porphyry copper control areas of different ages in central SouthAmerica. Modified after Camus (2002), Mpodozis and Ramos (1989), Perellóet al. (2003), and Sillitoe (1988, 1989). Black dots indicate porphyry copperdeposits. Control area ages: Permian-Triassic, 291 to 239 Ma; Paleocene, 64to 51 Ma; Eocene-early Oligocene, 44 to 29 Ma; Miocene-early Pliocene, 16to 4 Ma.
area boundaries are typically extended using interpolated ge-ology and geophysical surveys, such as aeromagnetics, toidentify where younger rocks or sediments conceal permis-sive rocks. Scale of the maps used to delineate control areascan have a strong effect on the extent of cover portrayed, asdetailed maps commonly show more cover than regionalmaps. Proportions of the control areas that are covered arelisted in Table 1.
Porphyry copper deposits are large-tonnage, disseminatedcopper sulfide deposits that have a large aerial extent and theirassociated alteration zones are even larger (see Singer et al.,2002). Due to their large aerial extent, porphyry copper depositslocated mostly under younger cover can have part of their alter-ation zones exposed. To prevent miscounting deposits that be-long to the exposed control areas, we estimate the percent ofcover associated with each deposit and count only those de-posits that are at least 50 percent exposed. Although arbitrary,this rule can be consistently applied, and the resulting densityestimates are based on known deposits and prospects that areconsistent with the grade and tonnage models and are locatedin exposed parts of control areas. Of the 339 porphyry copperdeposits and prospects that are in the 19 delineated controlareas, 16 percent or 55 deposits belong to the covered popula-tion (Table 3) and are not included in the analysis.
Table 1 includes the area (in km2) and the percent of eachcontrol area covered by postmineral rocks, sediments, andice. The proportions of different deposit subtypes and theapproximate number of unexplored or poorly exploredprospects allow an estimate of percent of explored deposits(Table 3). Estimates of porphyry copper deposit density (indeposits/km2; Table 1) are based on a ratio of the number ofexposed deposits plus prospects to the total exposed controlarea. Porphyry copper deposits located in each control areaare listed in Table 3, as well as estimated deposit ages andpercent cover for each deposit. Percent cover by deposit, asopposed to cover in control areas, was measured from de-tailed maps and cross sections of deposits as presented inpublications cited by Singer et al. (2005).
Porphyry Copper Deposit DensitiesThe number of exposed porphyry copper deposits and
prospects in each control area, adjusted for cover, provide thedeposit densities listed in Table 1. We have scaled the esti-mates by multiplying each by 100,000 (i.e., the number ofporphyry copper deposits per 100,000 km2 of permissiverock) so that the estimates represent whole numbers of
deposits and can be more easily discussed and remembered.A histogram of porphyry copper densities (Fig. 4) demon-strates a skewed distribution that has been documented forother deposit types (e.g., podiform chromite deposits: Singer,1994). With skewed distributions such as in Figure 4, a fewhigh values have a large influence on the mean density. How-ever, the mean is only one measure of central tendency; themedian is often a preferable measure. Using the estimates inTable 1, we can provide probabilistic estimates of porphyrycopper density that are not adversely affected by a few largevalues. Ninety percent of the control areas have densities offour or more deposits, 50 percent have densities of 15 or moredeposits, and 10 percent of the control areas have densities of35 or more porphyry copper deposits per 100,000 km2.
Effects of erosion
It is reasonable to assume that erosion would affect sizesand densities of porphyry copper deposits. If exposure to ero-sion over time removed deposits, there should be a lowerdensity of deposits in older control areas. Also, if parts of de-posits were removed by erosion or tectonics over time, thereshould be smaller deposits in older control areas. However, inan earlier study, we demonstrated that sizes (tonnages) ofporphyry copper deposits are not lower in older deposits(Singer et al., 2002). The midrange of ages on the deposits(Table 3) in each control area versus the deposit density of thebelt has a fitted regression line that slopes downward, but theslope is not significantly different than zero at the one percentlevel. Thus, we reject the hypothesis that deposit densities de-crease through time. Effects of erosion cannot be demon-strated by diminished sizes of porphyry copper deposits or bylower deposit densities through time.
One might expect that, due to erosion, the density of de-posits that formed in shallow environments might be less thanthe density of more deeply formed deposits. One way to testthis is to plot the percent of all porphyry copper deposits thatbelong to the gold-rich class because these tend to form inshallower environments than the other porphyry copper de-posits (Cox and Singer, 1992). However, gold-rich porphyrycopper deposits are not less common through time (Singer etal., 2002). Moreover, the proportion of gold-rich porphyrycopper deposits (Table 3) by control area versus the depositdensity of the control areas has a slope that is not significantlydifferent than zero, suggesting that control areas with shal-lower porphyry copper emplacement depths do not havelower densities of deposits.
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1
2
3
4
5
0
Cou
nt
Number of Deposits per 100,000 km 20 8 16 24 32 40 48 56 64 72 80 88 96 104 112 120 128 136
FIG. 4. Histogram of porphyry copper deposit densities per 100,000 km2.
Effect of map scales
When samples are taken that represent small areas, large dif-ferences will be present from sample to sample according tosampling theory. Thus, the scale of observations should affectvariability of mineral deposit densities. In situations where thereare a number of large well-explored regions, one would expectmuch lower variability among the estimates of deposit density.
The relationship of sample size affecting deposit densitieswas recognized by Agterberg (1977) in his studies of vol-canogenic massive sulfide deposits of the Abitibi region ofCanada and was discussed extensively by Bliss and Menzie(1993) in terms of distributions and spatial correlations of sev-eral deposit types. These studies of frequency distributionsand spatial correlations are typically concerned with variabil-ity within mineral deposit districts, whereas the present studyis concerned with variability among large areas containing anumber of deposits and districts.
Consideration of scale effects on the variability of depositdensities is meaningful only where the samples are a randomselection of all possible control areas. In this study, controlareas range in size from 1,600 to 470,000 km2, with map scalesranging from 1:240,000 to 1:2,500,000. Each control area con-tains at least one known porphyry copper deposit. Porphyrycopper deposit density is inversely related to area exposed asshown by the significant linear relationship in a log-log plot(Fig. 5). A similar negative relationship between area and de-posit density was found for podiform chromite deposits(Singer, 1994). We are not certain why this relationship holds,but the relationship must result in part from the rules used todefine control areas, which depend on map scale and infor-mation content. When large control regions are represented atsmall scales, units representing magmatic arcs may includerocks that do not belong to the magmatic sequence. Largerscale, more detailed representation of large control areas willexclude such rocks and result in a higher deposit density.Smaller control areas are less susceptible to this problem.
Adjustments for scale
Sampling theory, previous studies of density of podiformchromite deposits, and the relationship shown in Figure 5
indicate that deposit density is inversely related to permissivearea. On close examination of Figure 5, the negative relation-ship seems to be affected by one point (Puerto Rico). How-ever, even when this point is excluded the negative relation-ship between density and size of belt remains significantlydifferent than zero. Thus, we can use a linear regression ofpermissive area to estimate the number of deposits, similar tothe way used to estimate the number of podiform chromitedeposits (Singer, 1994). The linear regression line and confi-dence limits for individual permissive areas based on the 19control areas are provided in Figure 6. Estimates of the num-ber of porphyry copper deposits can be made from Figure 6by using the permissive area on the X axis projected to the 90percent confidence limit for a lower estimate of number ofdeposits, to the regression line for the 50 percent estimate,and to the 10 percent confidence limit for an upper estimate.To be more precise than can be shown in the log-log plot, thefollowing two equations are provided:
where area is the area that is permissive in square kilometers,the mean area is 4.897, t (Student’s t at the 10% level with 17degrees of freedom, t10,17df) is 1.740, sy|x (standard deviation ofnumber of deposits in given area) is 0.2700, n = 19, sx
2 (vari-ance of area) is 0.2916, and the R50, L90, and U10 estimates areused as exponents to the power of 10. For example, if the per-missive area is 25,000 km2, then the 50th percentile estimatewould be seven deposits (that is, log10R50 = 0.8375 or–1.1167 + 0.4443 log10(25,000). The 90th percentile estimatewould be two deposits (that is, log10L90 = 0.8375 –1.740•0.2700 ÷ (1 + (1/19) + (4.398 – 4.897)^2 /18•0.916),and the 10th percentile estimate would be 21 deposits. Theseestimates can be approximated from Figure 6.
A second approach is applicable only to certain sized per-missive areas near the median of the control areas. For per-missive tracts that have areas between 40,000 and 90,000 km2,90 percent of the control areas have densities of four or moredeposits, 50 percent have densities of 15 or more deposits,and 10 percent of the control areas have densities of 35 ormore porphyry copper deposits per 100,000 km2. In this case,the estimates can be made by using the generalized histogram(Fig. 4), as seen in the following example. Using the general-ized histogram, a permissive area of 75,000 km2 would have90, 50, and 10 percentile-estimated number of deposits offour deposits × (75,000/100,000 km2) or three deposits, 15 de-posits × (75,000/100,000 km2) or 11 deposits, and 35 deposits× (75,000/100,000 km2) or 26 deposits. Use of the relationshipbetween area and number (eqs. 1, 2) in this example gives es-timates that are about the same, that is four, 11, and 34 de-posits at the 90th, 50th, and 10th percentiles.
Exploration effects
Without closely spaced grid drilling, no area can be consid-ered with certainty to be completely explored. However, ifmodern exploration methods have been applied to a region
PORPHYRY COPPER DEPOSIT DENSITY 505
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Permissive area exposed (1,000 km )2
1 3.2 100 320 1,00010 321.8
10
5.6
3.2
100
180
56
32
18
Num
ber
of p
orp
hyry
Cu
dep
osits
/ 1
00,0
00 k
m2
FIG. 5. Porphyry copper control area exposed vs. density of deposits.
and the porphyry copper deposits are exposed, then few de-posits may remain to be found. Density estimates in somecontrol areas, such as Arizona and Puerto Rico, are unlikelyto change in any noticeable way due to new discoveries.That does not mean that deposits will not be found in thesecontrol areas, just that any new discoveries are likely to beunder cover and thus will not change the estimates of de-posit density. Some areas permissive for porphyry copperdeposits that recently have many new discoveries reportedsuch as southwestern and northern China and parts of Mon-golia (Singer et al., 2005) were excluded in this analysis be-cause their densities might change significantly as ongoingexploration continues. A number of other belts or parts ofbelts also were excluded because we believe that explorationfor exposed deposits is incomplete (e.g., Indonesia, Colom-bia, and Iran).
For most of the control areas chosen in this study, a few ad-ditional discoveries would not have much effect on the esti-mated deposit density. One additional discovery would in-crease the density for the eastern China control area from twoto three deposits per 100,000 km2. The deposit density of thiscontrol area also could increase due to refinement and shrink-age of the poorly defined permissive area. For several rea-sons, including high relief and a considerable ice cover, theYulong-Yunnan or Himalayan control area in China is onlynow undergoing extensive exploration, which could lead toseveral new discoveries. It is important to recognize, however,that even if all of the upward adjustments suggested for thesecontrol areas take place, the frequency of densities and the
relationship between densities and permissive areas probablywill not significantly change.
Applying Deposit Densities in an Assessment: An Example in Antarctica
Deposit densities for porphyry copper deposits presentedin this study provide a fundamental baseline that can be usedwith some confidence in assessments of undiscovered de-posits. In the example of Antarctica, no known porphyry cop-per deposits are present that have been drilled because min-eral exploration is forbidden.
In Antarctica there is a permissive belt of rocks similar tothose in the Andes of South America. The widespread Meso-zoic and Cenozoic intrusive and volcanic calc-alkaline rocks ofthe Antarctic Peninsula and eastern Ellsworth land is consid-ered a southward extension of the Andes of western SouthAmerica (Rowley et al., 1975b). On the basis of a 1:5,000,000scale geologic map by Craddock (1972), we estimate that thepermissive belt is about 1.7 Mkm2 in extent, including shelfice, permanent snow and ice, and water. The land areas, in-cluding areas mapped as permanent snow and unknown geol-ogy cover about 1 Mkm2. Exposed parts of the Andean belt inAntarctica are about 76,000 km2 in extent. Using equations 1and 2, and a permissive area of 76,000 km2, we estimate thatthere is a 90 percent chance of at least four, a 50 percentchance of at least 11, and a 10 percent chance of at least 34undiscovered porphyry copper deposits in this belt. Althoughno porphyry copper deposits have been thoroughly exploredin Antarctica, a number of prospects have been documented.
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Permissive Area (1,000 km )2
1 1,000560320180100563218105.63.21.8
10
100
1
1.8
3.2
5.6
0.56
18
32
56
180
0.32
Num
ber
of
Dep
osi
ts
Log
Num
ber
of
Dep
osi
ts
90 percent confidence limit
10 percent confidence limit
2.25
2
1.75
1.5
1.25
1
0.75
0.5
0.25
0
-0.25
-0.53 3.25 3.5 3.75 4 4.25 65.755.55.2554.754.5
Log Area
FIG. 6. Porphyry copper control area exposed vs. number of deposits with 90 and 10 percent confidence limits for num-ber of deposits.
Pride et al. (1990) examined four prospects in the northernAntarctic Peninsula; three additional prospects are docu-mented by Rowley et al. (1975a, 1988). Rowley et al. (1991)summarized these prospects and discussed about 25 addi-tional occurrences that have some characteristics in commonwith porphyry copper deposits. Thus, the above estimates ofnumber of undiscovered porphyry copper deposits on thebasis of deposit densities are reasonable. It is necessary, how-ever, to note that the example does not represent a situationrequiring adjustment of estimates. If estimates of the numberof undiscovered porphyry copper deposits were made for apermissive belt where there were known deposits, it would benecessary to subtract the known deposits from the estimate.
Summary and ConclusionsJust as possible tonnages of undiscovered deposits can be
estimated from a grade and tonnage model developed fromwell-explored deposits, possible numbers of undiscovered de-posits can be estimated from models of the frequencies of de-posits per square kilometer in well-explored permissive ter-ranes. Regions containing porphyry copper deposits, calledcontrol areas in this study, were delineated by the borders ofhosting magmatic arc terranes. The control areas were de-fined by regional zones of porphyry copper-related intrusionsthat are approximately the same ages as the porphyry copperdeposits and by distributions of major structures. Frequen-cies of deposits per unit area in 19 control areas were calcu-lated, including prospects that are likely to be included in fu-ture grade and tonnage models when the prospects aredrilled. Ninety percent of the control areas have densities offour or more deposits per 100,000 km2, 50 percent have den-sities of 15 or more deposits, and 10 percent of the controlareas have densities of 35 or more deposits. For permissivebelts between 40,000 and 90,000 km2 in extent, numbers ofdeposits can be estimated using the deposit density his-togram. The number of porphyry copper deposits per unitarea is inversely related to the area of exposed permissive ter-rane—similar to the negative relationship previously foundfor podiform chromite deposits. For porphyry copper de-posits, a linear regression line and confidence limits con-structed with all 19 control areas can be used to estimate thenumber of undiscovered deposits in a given area of permis-sive rock. For permissive areas that are less than 40,000 orlarger than 90,000 km2 in extent, the regression equation re-lating area permissive to the number of deposits is recom-mended to avoid biased estimates.
It is reasonable to ask how much reliance should be placedin these estimates. Estimates of the density or number of de-posits based on the model developed here could be slightlyhigher if a few more deposits were discovered in the exposedparts of the control areas, but such changes are unlikely tohave a significant effect on the densities or the inverse rela-tionship between the number of porphyry copper depositsand permissive area. Use of the relationship between numberof deposits and area permissive for porphyry copper depositswas demonstrated in part of Antarctica. Because of the un-usual status of Antarctica, some of the difficulties that mightbe encountered in exploration planning or assessments werenot evident in the example. Although not a common difficulty,perhaps the largest possible source of error in the application
of the regression equation would be when the region beingconsidered may not really be permissive for porphyry copperdeposits. For example, the regression equation applied to thelarge area of Permian to Carboniferous arc rocks in Queens-land, Australia, would lead to some high estimates of thenumber of deposits. However, despite many known prospectsand exploration over many years, no thoroughly drilled de-posits have been announced in this belt of rocks. In explo-ration planning or resource assessment, the regression equa-tion would typically be placed in a spreadsheet and used toestimate the number of undiscovered deposits in a partiallyexplored region for which a permissive area has been esti-mated. If the region being assessed contains known depositsthat are at least 50 percent exposed, that number would besubtracted from the regression estimates. In general, thenumber of known deposits likely to included in the grade andtonnage model and that meet the 2-km rule for combiningadjacent deposits should be subtracted from the regressionestimates in order to provide unbiased probabilistic estimatesof the number of undiscovered deposits. A final question thatarises in exploration and assessment is whether the local den-sity of deposits, rather than the general model presentedhere, should be used to estimate the number of undiscovereddeposits in a region. As a rule, the general model should beaccepted unless the local case can be shown to be significantlydifferent than the model. Estimates of numbers of depositsmade with the regression equation provide the best generalestimates and can be used as guides that allow rather simpleand robust estimation of the number of undiscovered por-phyry copper deposits.
AcknowledgmentsWe wish to thank K. J. Schulz and J. A. Briskey of the U. S.
Geological Survey for the support of V. I. Berger for part ofthis work. We gratefully acknowledge Drs. James Rytuba, TedTheodore, Darryl Lindsay, Jose Perelló, and Richard Leveillefor their helpful reviews. April 22, 2004; March 22, 2005
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(1) Western Philippines
The Western control area (11–1.5 Ma), as well as the East-ern control area, are generally delineated by the spatial dis-tribution of coeval calc-alkaline volcanic rocks and diorite toquartz diorite intrusions (Mitchell and Leach, 1991). Bound-aries of the control area correspond to outlines of subductedMiocene to Pleistocene slabs projected in Figure 2 (Mitchelland Balce, 1990; Rangin, 1991). The control area is dividedinto two parts by the prominent bulge of the North Palavanmicrocontinental block that includes the Tablas and Sibuyanislands at the eastern end (B.R. Berger, unpub., 2003). Thenorthern part of the control area encircles the western half ofLuzon Island and is related to the Manila trench subductionzone, whereas the southern part is possibly connected to theNegros trench. This control area contains 16 known porphyrycopper deposits and one prospect. The principal map used fordelineation of the control area was by the Geological SurveyDivision of Philippines (1963). The paper by Sillitoe andGappe (1984) also assisted in delineating Philippine controlareas.
(2) Central Philippines
The central Oligocene to early Miocene control area(30–18 Ma) contains 13 known deposits and two prospectsand extends from eastern Luzon to western Mindanaothrough the axial part of the Philippine archipelago. The con-trol area was delineated by the extent of felsic to intermedi-ate volcanic and intrusive rocks that are dated or mapped as30 to 18 Ma. The belt is partially overprinted by younger vol-canoes that contain no known porphyry copper mineraliza-tion. Rocks or sediments younger than 18 Ma were treated ascover. According to a recent model by Hall (2002) this con-tinuous arc could be related to eastward subduction of theProto-South China Sea plate. An interesting feature of thiscontrol area is the relationship of porphyry copper depositsnot only with typical diorite to quartz diorite intrusions butalso with alkaline rocks confined to a volcanic field at thesouth end of the Cagayan rift in eastern Luzon, which in-cludes the Dinkidi and Marian deposits (Wolfe et al., 1999).The principal map used for delineation of the control areawas by the Geological Survey Division of Philippines (1963).
(3) Eastern Philippines
The Eastern control area (10–3.5 Ma), including easternMindanao, Samar, and other islands bordering the Philippinetrench, is probably related to westward subduction of thePhilippine Sea plate. This control area has eight known de-posits and one prospect and is generally delineated by thespatial distribution of coeval calc-alkaline volcanic rocks anddiorite to quartz diorite intrusions (Mitchell and Leach,1991). The principal map used for delineation of the controlarea was by the Geological Survey Division of Philippines(1963). This area has a much lower deposit density than the
other Philippine belts (Table 1). This might be explained bythe generally lower level of exploration in the remote south-eastern part of Philippines. The possibility of undiscovereddeposits in the Eastern control area is confirmed by recentdiscoveries in northeastern Mindanao (Josef, 2002). The dif-ference in deposit density might also be related to the factthat the Central and Western belts originated by discontinu-ous eastward subduction of the South China Sea plate bring-ing fragments of Asian continental crust (Rangin, 1991; Hall,2002), whereas the origin of the Eastern belt is explained bywestward subduction of the intraoceanic Philippine Sea plate(Rangin, 1991).
(4) Northern Sulawesi, Indonesia
The north arm of Sulawesi is one of the most explored areasin Indonesia as reflected by two drilled porphyry Cu-Au de-posits and one prospect and numerous explored gold de-posits. The Tombulilato deposit, commonly referred to as adistrict (Perelló, 1994), consists of the closely spaced CabangKiri, Cabang Kanan, Sungai Mak, and Kayubulan Ridge de-posits. They are considered as one deposit here because thereis less than 2 km separating their alteration zones. NorthernSulawesi is a complex island arc developed by multistage andmultidirectional subduction from Eocene to the present(Kavalieris et al., 1992; Hall, 1996, 2002; Pearson and Caira,1999; Simandjuntak and Barber, 1999). Formation of calc-al-kaline volcanic rocks and porphyritic diorite intrusions, aswell as related copper deposits, mostly occurred in a period oftectonic-magmatic activity from 9 to 2.5 Ma that was relatedto the subduction of microplates under the North Arm alongthe Sulawesi and Sangihe troughs. The western border of theNorth Arm is delineated by the regional Palu-Koro fault di-viding different tectonic units of the island. The eastern bor-der is delineated by the currently active volcanism along lati-tude 1° N (Hall, 2002; Kavalieris et al., 1992; Simandjuntakand Barber, 1999).
Northwestern Cordillera in general
Two contiguous porphyry copper control areas of differentages in the Canadian Cordillera in British Columbia and ad-joining Washington extend over 1,700 km. They are namedthe Quesnellia and Stikinia control areas after their main-hosting terranes. The latter, younger area, is located betweenthe Pacific coast and the older control area to the east. Mapsused for delineation of these areas were by Jackson (1982),Monger and Berg (1987), and Nokleberg et al. (1994).
(5) Quesnellia-British Columbia, Canada, and adjoiningWashington, United States
The Quesnellia control area contains 31 porphyry copperdeposits and seven prospects of Late Triassic to Middle Juras-sic age (220–172 Ma). We believe that the ages of two de-posits with younger reported ages (Kwanika, 121 Ma, and
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APPENDIX
Description of Control Areas
Brenda, 143 Ma) need to be verified. Sixty-three percent ofthe deposits and prospects, including predominant copper-gold porphyries, are here related to alkaline diorite-mon-zonite-syenite plutons accompanied by trachyandesite andvarious alkaline volcanic rocks. The remaining deposits arerelated to calc-alkaline diorite-quartz diorite-granodioriteplutons accompanied by andesite, dacite, rhyodacite, and rhy-olite. Both groups of contemporary intrusions (Late Triassicto Middle Jurassic) and coeval porphyry copper deposits areconfined to Quesnellia and Cache Creek terranes, and thecontrol area is delineated along boundaries of these terraneswhich are composed of fragments of the late Paleozoic andearly Mesozoic island arcs (Monger et al., 1982; Nokleberg etal., 1994; 2000; Lang et al., 1995; McMillan et al., 1995).
(6) Stikinia-British Columbia, Canada
The control area includes 17 porphyry copper deposits andthree prospects of Late Cretaceous to Paleocene age (88.5–53Ma). About 50 percent of the porphyry copper deposits in theQuesnellia control area are the gold-rich variety, whereas inthe Stikinia control area less than 20 percent are gold rich andover 60 percent belong to the general copper-dominated por-phyry subtype. The Stikinia belt also has different proportionsof igneous rocks associated with deposits. About 90 percent ofthe deposits and prospects are related to calc-alkaline diorite-quartz diorite-granodiorite plutons, and only two deposits areassociated with alkaline diorite-monzonite-syenite plutons.The large Stikinia terrane consists of and is confined to ac-creted fragments of Devonian to Jurassic island-arc volcanicand sedimentary assemblages overprinted by the youngermagmatic arc (Monger et al., 1982; Nokleberg et al., 1994;2000; Lang et al., 1995; McMillan et al., 1995). An outline ofthe Stikinia terrane is used to delineate the control area.
(7) Arizona-north Mexico
The belt contains 54 porphyry copper deposits and threeprospects primarily located in the southern part of Arizonaand in northern Mexico in the states of Sonora and Sinaloa.One of the prospects occurs in the southeastern corner ofNevada and four deposits are in western New Mexico; all arewithin the Late Cretaceous to Eocene time interval with de-posit ages of 75 to 51 Ma. The belt is delineated by the dis-tribution of Late Cretaceous to Eocene felsic intrusions andporphyry copper deposits (Laramide metallogenic epoch).The shape of the belt is mushroomlike with the northern“hat” extending west-northwest for 830 km along the south-ern edge of the Colorado plateau (possibly along a fault sys-tem of the Texas lineament) and the “stem” extending for1,100 km to the southwest along the west coast of Mexico.The northern part of the belt has a complex spatial pattern ofdeposit ages, whereas in the long southern part porphyry de-posits generally are younger toward the continent. The de-posits are related to high potassium calc-alkaline monzonite-diorite-granodiorite plutons accompanied by calc-alkalinevolcanic rocks. Intrusive and extrusive rocks related to theCretaceous-Cenozoic magmatic arc developed along an ac-tive western margin of the North American continent (Coney,1978; Ruiz, 1986; Dickinson, 1989; Titley and Anthony, 1989;
Titley, 1995). Maps used for delineation on the control areawere by Clemons et al. (1982), Dirección General de Ge-ografía del Territorio Nacional, 1981 (1981), Ramos (1976),and Richard et al. (2000).
(8) Puerto Rico
The small Puerto Rico porphyry copper control area is wellstudied (Cox, 1993). There are two explored copper-gold de-posits. Mineralization is related to tonalite and quartz dioriteporphyry intrusions of Eocene age (42 and 41 Ma) belongingto the Cretaceous to Eocene island-arc volcanic-plutonic as-semblage. The map used to delineate this assemblage is byCox and Briggs (1973).
(9) Ecuador and northern Peru
This belt is confined to and delineated by the Cenozoicmagmatic arc bordering a northwestern active margin ofSouth America (Sillitoe, 1988, 1990; Petersen and Vidal,1996; Noble and McKee, 1999; Dirección Nacional de Ge-ología, 2000). The belt includes 12 porphyry copper depositsand eight prospects of Miocene age, ranging from 20 to 8 Ma,that are related to calc-alkaline diorite-quartz diorite-gran-odiorite intrusive rocks accompanied by andesite, dacite, andrhyolite porphyries and corresponding volcanic rocks. Mon-zonitic rocks are associated with only two of the deposits.Maps used for delineation of the control area were by Servi-cio Nacional de Geología y Minería de la República deEcuador (1969) and Instituto Geológico y Minero de laRepública del Perú (1975).
(10) Central Peru
The control area is a continuation of the Ecuador-NorthPeru control area with a gap between the two. The area oc-curs within the Cenozoic magmatic arc of South America (Sil-litoe, 1988, 1989; Petersen and Vidal, 1996; Noble andMcKee, 1999), entirely in a flat subduction region (James andSacks, 1999). The belt contains six deposits and eightprospects of Miocene age, ranging from 21 to 8 Ma. The out-line of the control area is defined by the locations of porphyrycopper deposits in association with high potassium calc-alka-line monzonite-diorite-granodiorite intrusive rocks accompa-nied by andesite and dacite volcanic rocks. Various mon-zonitic rocks are common and are missing in only threedeposits. The map used for delineation of the control areawas by Instituto Geológico y Minero de la República del Perú(1975).
(11) Southern Peru and north-central Chile
The control area is confined to the Paleocene continentalmagmatic arc (Fig. 3; Sillitoe, 1988, 1989; Clark et al., 1990;Espinosa et al., 1996; Petersen and Vidal, 1996; Noble andMcKee, 1999). It includes 11 deposits and seven prospects ofPaleocene age, ranging from 64 to 51 Ma. The boundary ofthe control area is based on the distribution of the coevalstocks of calc-alkaline diorite-quartz diorite-granodiorite in-trusive rocks and associated dacite and rhyolite porphyries
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and andesitic volcanic rocks. Monzonitic rocks are known inthree deposits. Maps used for delineation of the control areawere by Instituto Geológico y Minero de la República delPerú (1975), Instituto de Investigaciones Geológicas (1982),and Zappettini et al. (2001).
(12) Southeastern Peru and eastern Chile
This control area of middle Eocene to early Oligocene age(44–29 Ma) contains 17 porphyry copper deposits and 14prospects, including the giant copper producers Chuquica-mata, Collahausi, El Salvador, and La Escondida. The controlarea consists of two main parts, the Andahuaylas-Yauri belt insoutheastern Peru and a long belt in Chile controlled by theDomeyko strike-slip fault system (Sillitoe, 1988; Zentilli andMaksaev, 1995; Perelló et al.; 2003; V. Maksaev, writ. com-mun., 2001). These parts are divided by Miocene-Quaternarysediments and volcanic rocks. The control area extendsslightly eastward to include the Taca Taca deposits in Ar-gentina (Fig. 3), which are early Oligocene in age (33.6–29Ma: Rubinstein et al., 1999; Blower, 2003). Generally, the de-posits are related to typical calc-alkaline diorite-quartz-dior-ite-granodiorite with subordinate monzonitic rocks. Monzo-granite and quartz monzonite porphyry are present as distinctrock types in four of the deposits. Porphyry stocks of this agethat define the belt are not accompanied by coeval volcanicrocks but are associated only with sparse tuff beds (Mpodozisand Ramos, 1989). Maps used for delineation of the controlarea were by Instituto Geológico y Minero de la Repúblicadel Perú (1975), Instituto de Investigaciones Geológicas(1982), and Zappettini et al. (2001).
(13) Eastern Chile and westernmost Argentina
The youngest porphyry copper deposits of this control area(Miocene-early Pliocene; 16–4 Ma) occur within a wide controlarea in eastern Chile and contiguous westernmost Argentina.The delineation of the control area was based on the depositdistributions and on the outline of coeval magmatic rocks. Theeastern border of the control area, bending around the impor-tant Farallón Negro porphyry copper district in western Ar-gentina (Sasso and Clark, 1998), is not well defined due to thepoorly known distribution of Miocene to Pliocene porphyrystocks in the interior of the western part of the continent. Thebelt includes 12 porphyry copper deposits and 11 prospectsthat are principally hosted by calc-alkaline dacite and andesiteporphyry intrusions accompanied by subaerial extrusive rocks.Dioritic rocks and potassium-rich monzonitic rocks are presentin eight deposits including the large El Teniente, Los Bronces-Río Blanco, Los Pelambres, and El Pachon deposits. Mapsused for delineation of the control area were by Instituto de In-vestigaciones Geológicas (1982), Servicio Geológico MineroArgentino (1999), and Zappettini et al. (2001).
(14) Chile and western Argentina
The late Paleozoic to Triassic control area in western Ar-gentina was first outlined by Sillitoe (1977, 1988) along dis-continuous fragments of late Paleozoic terranes and datedporphyry copper deposits. Camus (2002) indicated that there
are additional Paleozoic porphyry copper prospects in Chile.Our delineation of the control area includes most of the latePaleozoic magmatic arc reconstructed along the western mar-gin of South America by Mpodozis and Ramos (1989) andmodified by Camus (2002), including Chile and western Ar-gentina. Volcanic and associated intrusive rocks belonging tothe late Paleozoic to Triassic Gondwana magmatic arc weresuperimposed on the accreted Paleozoic continental Chileniaterrane (Ramos et al., 1986). This north-trending belt isroughly parallel to late Mesozoic to Cenozoic porphyry cop-per belts and is partially overprinted by younger magmaticrocks and related mineralization. In the southern part of thecontrol area, for example, two younger porphyry copper de-posits, Campana Mahuida (74 Ma) and Quebrada del Bronce(45 Ma), are located around 37º 30’S latitude (Fig. 3) insidethe discontinuous borders of the control area to the north ofthe Permian La Voluntad deposit. The two known porphyrycopper deposits and six prospects range in age from 291 to239 Ma, with the mineralization related to calc-alkaline dior-ite-quartz diorite-granodiorite intrusive rocks. The maps usedfor delineation of the control area were by Instituto de Inves-tigaciones Geológicas (1982), Servicio Geológico Minero Ar-gentino (1999), and Zappettini et al. (2001).
(15) Central and south Europe, Carpathian-Balkan
The Laramide Late Cretaceous Carpathian-Balkan por-phyry copper control area is part of the global TethyanEurasian metallogenic belt (Jankovic, 1980, 1990; Sillitoe,1980; Vassileff and Stanisheva-Vassileva, 1981). The belt, out-lined by mineralization and coeval igneous rocks, bends fromsouthern Romania, through Serbia, Bulgaria to northwesternTurkey. It includes eight porphyry copper deposits and 13prospects with ages of 82 to 70 Ma. The porphyry copper de-posits are hosted in calc-alkaline (banatitic) diorite-quartzdiorite-granodiorite ± monzonite porphyry intrusions associ-ated with andesite to dacite volcanic rocks (Cioflica and Vlad,1980; Karamata et al., 1997; Berza et al., 1998). There is con-flicting evidence concerning whether the belt represents anAndean- (Márton, 1997) or island arc-type subduction-re-lated magmatic arc (Vlad, 1997), or perhaps Late Cretaceousrifting (Popov, 1996). Maps used for delineation of the controlarea were by Ketin (1961) and Ciric et al. (1969).
(16) Central Kazakhstan
The control area is outlined by the Jungar-Balkhash tec-tonic province and its adjacent areas containing igneous rocksfrom Late Devonian to Carboniferous continental marginalmagmatic belts and island arcs (Zonenshain et al., 1990). Itcontains 10 porphyry copper deposits and six prospects(354–264 Ma) that are primarily related to Carboniferouscalc-alkaline diorite-quartz diorite-granodiorite-granite ±monzonite associated with andesite to rhyolite volcanic rocks.Monzonitic rocks are noted in 25 percent of the deposits thatare generally related to diorite-granite-granodiorite intru-sions. The delineation of the control area would be improvedby additional modern data on deposit ages. Maps used for de-lineation of the control area were by Il’in (1967), Bespalov etal. (1971), and Nalivkin and Sokolov (1983).
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(17) East China
The control area encompasses the Changjiang (YangtzeRiver) fold belt dividing the North and South China cratons,and the narrowed eastern block of the latter, that were acti-vated by the late Mesozoic Yanshanian orogeny. The controlarea is delineated by the distribution of Jurassic to Early Cre-taceous plutonic rocks of the magnetite series (Pan and Dong,1999) and by the locations of three related porphyry copperdeposits and two prospects of 159 to 105 Ma age. The north-ern border of the control area is delineated by the southernpart of northern China craton. The southern boundary istraced along regional faults that separate the South China cra-ton from the Caledonian fold system to the south that con-tains distinctive S-type Yanshanian granites and related Sn-W-Mo deposits (Zhai et al., 1997). The porphyry copper depositstypically are hosted in calc-alkaline diorite-quartz diorite-gra-nodiorite ± monzonite porphyries. Monzonitic rocks areknown in two deposits. In addition to porphyry copper de-posits, the belt contains numerous ore deposits of differenttypes and ages that seem to be unrelated to porphyry miner-alization. Fan (1984) considered the East China plutonic beltto have formed as a result of the westward Mesozoic subduc-tion of the Pacific plate under South China. Locations ofJurassic to Early Cretaceous plutons and mineralization arecontrolled by regional faults related to the Circum-PacificMesozoic to Cenozoic active continental margin (Qiu et al.,1989; Pan and Dong, 1999). Maps used for delineation of thecontrol area were by Sun and Wu (1984) and Guo (1987).
(18) Yulong-Yunnan (Himalayan), China
The control area includes six Eocene to Oligocene (55–26Ma) porphyry copper deposits and nine prospects related to a
chain of high potassium calc-alkaline monzonite-granite por-phyry intrusions, in which monzonitic granite porphyry is thetypical host rock. The plutonic belt and related deposits areprobably controlled by late Mesozoic-Tertiary eastward-dip-ping subduction along the Himalayan fault system (Fan,1984; Zhang et al., 1984; Tang and Luo, 1995; Gu et al., 2003;Z. Rui, writ. commun., 2002;) that resulted in large-scalestrike-slip displacements (Hou et al., 2003). Maps used fordelineation of the control area were by Sun and Wu (1984)and Guo (1987).
(19) Mulong, NSW, Australia
The control area corresponds to the Ordovician to EarlySilurian Molong volcanic arc of the Lachlan fold belt(Bowman et al., 1983; Scheibner, 1998; Glen et al., 2003;Wilson, 2003). The arc consists of monzonite-gabbro-granodiorite-diorite intrusions associated with high potassiumshoshonitic basalt, trachybasalt, keratophyre, and pyroclasticrocks. Based on aeromagnetic data, the northern continuationof the arc is interpreted to be beneath the Mesozoic-Cenozoic cover (Scheibner, 1998). The control area has adistinctive complicated shape that is probably caused by latemultistage deformation. It includes two porphyry copperdeposits and four prospects that range from 447 to 370 Ma inage and are accompanied by coeval high-sulfidation gold,copper-gold-bearing skarn and other related deposit types.Maps used for delineation of the control area were by Pogson(1972) and Scheibner (1998).
