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Pilot Study Experiments Sourcing
Quartzite, Gunnison Basin, Colorado
Bonnie L. Pitblado,1, * Carol Dehler,2 Hector Neff,3
and Stephen T. Nelson4
1
Anthropology Program, Utah State University, 0730 Old Main Hill, Logan, UT
84322
2
Department of Geology, Utah State University, 4505 Old Main Hill, Logan, UT
84322
3
Anthropology Department, California State University Long Beach, 1250
Bellflower Blvd., Long Beach, CA 90840
4
Geology Department, Brigham Young University, S-389 ESC, Provo, UT 84642
This paper reports the results of pilot-study efforts to develop methods to profile quartzite, a
rock type to which geochemical and other sourcing techniques have only rarely been applied.
The long-term goal of the research is to fingerprint sources of quartzite in the Gunnison Basin,
southwest Colorado, used by Paleoindian people ca. 11,500–8,000 years ago to make stone
tools. Success would facilitate reconstruction of Paleoindian mobility in the Southern Rocky
Mountains and potentially anywhere prehistoric people used quartzite. The goals of this paper
are more modest: to demonstrate that a small-scale exploration of sourcing techniques suggests reason for optimism that quartzites may be amenable to source discrimination. For the
same twenty Gunnison Basin quartzite samples, this study evaluated petrography, ultraviolet
fluorescence (UVF), wavelength dispersive X-ray fluorescence (WD-XRF), instrumental neutron activation analysis (INAA), and inductively coupled plasma mass spectrometry—both
acid-digestion (AD-ICP-MS) and laser ablation (LA-ICP-MS)—as means to differentiate among
the specimens and the sources they represent. Although more testing is needed to verify and
refine our results, the study suggests there is potential for petrography, INAA, and both versions of ICP-MS to discriminate among quartzites from different source localities in the
Gunnison Basin. The greatest potential for discriminating among different sources of quartzite
in the Gunnison Basin may lie in a methodology combining petrographic analysis and LA-ICPMS. Future testing is required to evaluate this two-fold approach. © 2008 Wiley Periodicals, Inc.
INTRODUCTION
The archaeological literature is replete with studies from around the world that
have effectively invoked stone sourcing data to reconstruct how humans and their
tool-making hominid ancestors moved across the land (Odell, 2000, 2004). In effect,
such investigations track the people who procured, knapped, and used stone tools
by tracking movement of the stone itself away from a known geological source(s)
on a landscape. However, these undertakings have typically involved sourcing either
*Corresponding author; E-mail: [email protected].
Geoarchaeology: An International Journal, Vol. 23, No. 6, 742–778 (2008)
© 2008 Wiley Periodicals, Inc.
Published online in Wiley Interscience (www.interscience.wiley.com). DOI:10.1002/gea.20240
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volcanic (e.g., obsidian) or microcrystalline (e.g., chert) materials, both rock classes
with tried and true, if imperfect, sourcing track records.
Quartzite, a ubiquitous rock type worldwide and a common choice for prehistoric tool makers everywhere, does not enjoy a strong sourcing track record—or a
weak one. Quartzite has virtually no sourcing track record at all. In places like the
Upper Gunnison Basin of southwestern Colorado, where quartzite constitutes 95%
or more of many chipped stone assemblages (e.g., Andrews, 2005; Pitblado, 1994, 2000,
2002, 2003; Stiger, 2001, 2006), this problem and the interpretive limitations imposed
thereby are particularly acute. However, given the frequent use of quartzite by so
many prehistoric knappers (and grinders and sculptors)—and the parallel needs of
geologists working to characterize quartzite formations across often long distances—
it is fair to say that many earth scientists would benefit enormously from the ability
to fingerprint quartzites the way we currently do other rock types.
The purpose of this paper is to report key results of a pilot study that evaluated
six methods with potential to characterize quartzite sources and artifacts in Colorado’s
Upper Gunnison Basin: petrography, ultraviolet fluorescence (UVF), wavelength dispersive X-ray fluorescence (WD-XRF), instrumental neutron activation analysis
(INAA), and inductively coupled plasma mass spectrometry, both acid-digestion
(AD-) and laser ablation (LA-ICP-MS). In the sections that follow, we first define
geologic and geochemical terminology and then present an overview of applications
of various sourcing techniques to archaeological situations. We then turn to our primary goal: presenting our Gunnison Basin quartzite sourcing pilot study methods
and key results. Although we discuss in depth only our most robust results and those
that will drive our future research, all raw data from all of our analyses have been
archived at the geological community Web site https://www.paleostrat.org/
Public/projects.aspx?pide2bf1281-b96a-48ad-85e3-9b2a8df29b6a for public viewing
and downloading. In the final section of the paper, we draw those conclusions our
limited data set will support and offer suggestions for future research to advance
quartzite sourcing in Colorado’s Gunnison Basin and elsewhere.
GEOLOGIC AND GEOCHEMICAL TERMINOLOGY
When we discuss the “sourcing” of quartzite artifacts or otherwise reference stone
“sourcing,” we refer to efforts to match a rock sample of unknown origin to a geological source of that raw material with a high degree of statistical certainty. We
acknowledge and agree with Shackley’s (1998:261) point that “nothing is ever really
‘sourced.’” Stone sourcing is a statistical undertaking, and archaeologists can only
achieve lower or higher levels of probability that an artifact and source represent the
same rock population.
Because they form the crux of our pilot study methodology, we refer frequently
to “petrography,” and “geochemical” sourcing techniques. Petrography characterizes the composition and texture of rock macroscopically and microscopically and
yields data sets of primarily qualitative (but some quantitative) observations.
Geochemical techniques yield quantitative profiles of the elements present in a sample. Variability that uniquely “fingerprints” any stone type is usually not among its
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major or minor elements, but rather its trace elements, present in parts per million
or even smaller quantities. Some geochemical techniques are more sensitive to particular trace elements than other techniques, and some methods are better suited than
others for profiling particular materials.
For any project requiring chemical characterization of an object, the researcher
must choose the most appropriate of an array of geochemical analytical methods. In
archaeology, two techniques have been particularly frequently employed to profile
and source artifacts: XRF and INAA. Most trace-element analysis of obsidian is done
using XRF (though INAA of obsidian is preferred under certain conditions); characterization of pottery and its constituents often relies on INAA. ICP-MS—most
recently in minimally invasive LA-ICP-MS form (e.g., Speakman & Neff, 2005)—is a
powerful and increasingly popular means of chemically profiling an array of materials. As we will show and discuss, of all the techniques with which we experimented,
we conclude that AD-ICP-MS shows the greatest inherent ability to detect and measure diagnostic suites of trace elements in Gunnison Basin quartzite samples and
prehistoric artifacts, but that with methodological refinement, minimally invasive
LA-ICP-MS may prove to be the best overall choice for an archaeological sourcing
program that seeks to retain artifact integrity.
Finally, we wish to be clear about what we mean when we refer to “microcrystalline” rock types and, most important of all, “quartzite.” “Microcrystalline quartz”
(sensu Rapp, 2002) describes fine-grained, silica-rich rocks such as chert, flint, agate,
jasper, and chalcedony that form in a variety of geological environments and assume
a diverse spectrum of structures (e.g., nodules, lenses, beds), all of which were
exploited prehistorically (Luedtke, 1992). The terms “chert,” “cryptocrystalline material,” and “flint” are all roughly synonymous with Rapp’s (2002) “microcrystalline
quartz”; however, we invoke the latter term here because it more inclusively and
precisely describes the many fine-grained, often knappable rocks that contrast with
the quartzite we studied.
Like microcrystalline quartz, quartzites are also essentially pure quartz with a vitreous luster and (often) conchoidal fracture mechanics, but most have a distinctly
granular texture (Howard, 2005; Rapp, 2002). All quartzites, however, are not created
equal. The main types of quartzite, orthoquartzite and metaquartzite, are characterized by their distinct textures, which are indicative of their different origins (compare Plate 1C–F with Plate 2A–D in Carozzi, 1993). Understanding the textural and
potential geochemical differences between these two types of quartzite can have
implications for interpreting results of geochemical analyses.
Orthoquartzites are sedimentary rocks composed of dominantly quartz sand grains
(95%) and cemented by silica (Carozzi, 1993; Howard, 2005). All orthoquartzites have
a granular texture, with cement filling between the grains. The grain boundary contacts are usually tangential and surrounded entirely or in part by crystalline overgrowths of microcrystalline quartz. Although many orthoquartzites contain grains of
exclusively quartz, other grain types can be present in small amounts (typically 5%).
Such grain types include feldspar, rock fragments, micas, and heavy minerals. Quartz
grains come in a variety of types, including monocrystalline, undulose, and polycrystalline. Cements other than silica, such as hematite, may exist as a minor phase.
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All grains within an orthoquartzite, but especially the quartz grains, originated from
the weathering and transport of other sedimentary, plutonic, or metamorphic rocks.
Therefore, although a sedimentary quartzite has a distinctive texture, the grain composition (and hence the geochemical signature) can be quite variable and reflect an
igneous, metamorphic, sedimentary, or a mixed signature. Further, quartzites can
have multiple cement phases, all of which may contain unique geochemical signatures (Pettijohn, Potter, & Siever, 1987).
Metaquartzite is metamorphosed orthoquartzite or quartz sandstone, where heat
and/or pressure have obliterated original grain size, shape, and other characteristics, resulting in features that can be identified microscopically (Herz & Garrison, 1998;
Howard, 2005; Krynine, 1968; Rapp, 2002). Metaquartzites typically exhibit suturing
of grain boundaries and can show elongation of grains parallel to foliation planes or
other tectonic fabric. As with orthoquartzites, metaquartzites can have a variety of
quartz-grain types. Metaquartzites form when a quartz sandstone or orthoquartzite
changes by crossing temperature and/or pressure thresholds, causing silica dissolution
and compression of grains. By their nature, metaquartzites also have the potential
to reflect the geochemistry of grains that originated from a variety of geologic sources.
Additionally, metaquartzites may assume new elemental signatures from fluids that
were introduced during the metamorphic event(s).
Pertinently for our pilot study, both types of quartzites, although quartz-rich, have
origins that are sufficiently variable and unpredictable that geochemistry alone may
not make their origin discernable. This is not to say that a quartzite cannot be geochemically unique; rather, both types of quartzites can yield very similar or very
different elemental signatures, and geochemistry alone, as assessed through
any analytical technique, may not be able to discern one type from the other. This
anticipates a conclusion that we will draw later in this paper: that petrography is
important for “ground truthing” the results of geochemical analyses and will be an
important component of future quartzite-sourcing efforts.
ARCHAEOLOGICAL APPLICATIONS OF STONE SOURCING
Of three of the major rock types used by prehistoric people everywhere (obsidian, microcrystalline quartz, and quartzites) archaeologists have sourced obsidian
most often and with the greatest success. This is because unlike microcrystalline
quartz and quartzite, obsidians represent a single volcanic flow with a unique and
homogenous chemical signature and a limited geographic extent on a landscape—
ideal conditions for permitting the matching of artifacts from unknown sources to
geologic sources with distinct chemical profiles. As early as 1968, Frison et al. used
INAA to chemically profile obsidians from the northwestern Plains. Shortly thereafter,
Gordus (1970) successfully applied INAA to obsidian artifacts from Syria and Iraq.
In 1985, Wright and Chaya (1985) refined Frison et al’s (1968) findings by coupling
INAA with a technique that would soon come to dominate obsidian studies: XRF.
XRF, a cost-efficient approach to obsidian sourcing, has been used to geochemically characterize obsidian sources and artifacts from (among many other areas worldwide), the eastern United States (Hughes, 1992); east-central California (Hughes, 1994);
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the Great Basin generally (Jones et al., 2003); western Utah specifically (Nelson &
Holmes 1979); and New Mexico and the Southwest (Peterson, Mitchell, & Shackley,
1997; Shackley, 1995). Obsidian sourcing has come so far, in fact, that Peterson, Mitchell,
and Shackley (1997:241) noted that “it appears that there are no longer any significant
unlocated or ‘unknown’ sources in archaeological contexts in the U.S. portion of the
Southwest. It therefore follows that source characterization and data libraries as currently known and published can be trusted as accurate reflections of the availability
of archaeological obsidian in the prehistoric Southwest.”
Such absolute sourcing success does not apply to efforts to characterize and source
microcrystalline quartz materials. This is not because geologists and archaeologists
have not experimented with these rocks, but rather because microcrystalline quartz
forms under such a wide range of conditions that a technique that works well to characterize one source may not work at all elsewhere. Microcrystalline quartz can be so
chemically homogenous across a 1000-km-long formation that matching an artifact
to it, even with high statistical certainty, is useless for an archaeologist interested in
assessing the land use behavior of the knapper (e.g., Cackler et al., 1999; Shackley,
1998). On the other hand, microcrystalline quartz can be so chemically heterogeneous
that intra-source variability exceeds inter-source variability, making it impossible to
assign an artifact of unknown material to a source candidate (e.g., Roll et al., 2005).
Despite the geologically rooted challenges of chemically characterizing microcrystalline rocks and sourcing artifacts made of them, archaeologists have worked
extensively with them. Luedtke (e.g., 1978, 1979; Luedtke & Meyers, 1984) pioneered
INAA of microcrystallines, demonstrating statistically significant differences in traceelement profiles of cherts from the Midwest. Other archaeologists have used INAA
to effectively source microcrystalline artifacts from throughout North America (e.g.,
Hatch & Miller, 1985; Hoard et al., 1992, 1993 [but see Church, 1995, and Hoard et al.,
1995]; Lyons, Glascock, & Mehringer, 2003; Spielbauer, 1984). Cackler et al. (1999) AQ4
had less success using INAA to distinguish chemically homogenous cherts in northern Belize, but others have had encouraging results profiling microcrystalline quartz
via other geochemical methods, including XRF (Church, 1990, 1996; Warashina,
1992), Fourier infrared spectroscopy (Long, Silveira, & Julig, 2001), and LA-ICP-MS
(Rockman, 2003; Roll et al., 2005).
So where does quartzite fit into the sourcing picture? The answer is nowhere,
because so few have studied it. In a 20-year-old paper, Carol Ebright (1987:29) observed
that “quartzite is a major lithic material employed prehistorically throughout the
world from the time of the first stone tool manufacture . . . [but] archaeologists have
largely ignored this raw material with respect to proper identification, source location, basic lithic analysis, and replicative experimentation.” She was right, and the
situation has changed little in the intervening two decades. Reasons why quartzite has
been ignored by archaeologists are not rooted in empirical evidence demonstrating
that quartzite is unsourceable. There is very little literature, geologic or archaeological, related to quartzite sourcing, but the few extant studies document quartzitesourcing successes at least rivaling those achieved with microcrystalline quartz.
Petrography has proved useful for characterizing and distinguishing among
quartzite sources. Church (1990, 1996) studied not only microcrystalline quartz from
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the Bearlodge Mountains of Montana, but also quartzite. He subjected samples of both
to low-power microscopic examination, which he found to be of little help for characterizing cherts and chalcedonies, but quite helpful for quartzites, which he grouped
into four classes based on texture. Church (1996:141) concluded that “this method
seems to offer quick and easy sourcing to the parent formation for orthoquartzites
with a high degree of reliability,” a finding he corroborated and refined with XRF.
Similarly, Stross et al. (1988) coupled petrographic characteristics with INAA to differentiate quartzites from two ancient Egyptian quarries located 900 km apart.
As with petrography, when geochemical analyses have been applied to quartzite,
the results have shown them to be sufficiently sensitive to trace-element variability
to permit source profiling. Of his study of orthoquartzites, Church noted (1996:141),
“XRF can provide basic, valuable information.” A complementary study of quartzites
from a geographic area overlapping Church’s also invoked XRF. Schneider (2006:81)
concluded that “quartzites contained enough trace element variability to determine
which formation they originated from.” Finally, in keeping with the methods of Stross
et al. (1988), Julig, Pavlish, and Hancock (1987) employed INAA to characterize
quartzite artifacts from the Paleoindian Cummins Site, Ontario, and match them to
orthoquartzite from Wisconsin. In the few test cases on record, various methods have
effectively distinguished among quartzite sources—a finding our pilot study supports.
PILOT STUDY METHODOLOGY
Field and Laboratory Sampling Strategy
The purpose of our pilot investigation was to build on those few studies of quartzite
petrography and geochemistry that do exist and that suggest that discrimination of
sources may be possible. We elected to work with archaeological specimens from
the multi-component Chance Gulch site, located in the Gunnison Basin, 6.5 km
southeast of Gunnison, Colorado (e.g., Pitblado, 2002; Stamm, Pitblado, & Camp,
2004), and geologic samples from quartzite sources located both very nearby the
Chance Gulch site and relatively far away from it (Figure 1). Our goal was not, at this
beginning stage of quartzite-sourcing research, to sample the entire quartzite universe of the Gunnison Basin or anything close to it. Instead, we wanted to assemble
a modest data set of twenty quartzite samples that could be subjected to an array of
sourcing techniques to determine if one or more of the techniques proved effective
enough to justify larger-scale experimentation.
We compiled our twenty quartzite samples as follows. First, we selected eight
large quartzite flakes from a ca. 4500 rcybp (Middle Archaic) component at
the Chance Gulch site. The Chance Gulch site also contains an 8000 rcybp late
Paleoindian component, but (1) the Paleoindian flakes are smaller than their
Middle Archaic counterparts and none would have supplied enough material for
multiple forms of analysis, some destructive; (2) the Paleoindian component yielded
fewer artifacts than the Middle Archaic component, so analyzing eight Paleoindian
flakes would have destroyed a greater percentage of the older assemblage than it did
the younger; and (3) the long-term goal of our research is to source the Paleoindian
flakes and tools from the site to reconstruct the mobility patterns of the site’s earliest
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occupants. We wanted to refine our methodology before subjecting the Paleoindian
flakes to whatever form of analysis proves most suitable. At the same time, we saw
value in testing archaeological specimens from Chance Gulch to gain a preliminary
sense of whether their signatures would suggest a relationship to the nearby quartzite
quarries we have previously hypothesized drew people to the Chance Gulch site
through time (Pitblado, 2002).
Our remaining twelve quartzite samples derived from geologic sources of the
material located in some cases nearby the Chance Gulch site, in others at some distance from the site. Three sources (1, 3, and 4; Figure 1) are Tertiary gravel deposits
2 km from Chance Gulch (e.g., Figure 2a). All three show clear evidence of having
been quarried prehistorically. Source 2 (Figures 1, 2b) is an outcrop of Precambrian
quartzite 0.5 km northeast of Chance Gulch and neither quarried nor suitable for
knapping. The assemblage also included samples from two localities outside the
Gunnison Basin, one in Montrose County, Colorado (Source 5), the other outcrops
of Uinta Mountain Group quartzite from northeastern Utah (“UMG” samples), both
unlikely sources for Chance Gulch knappers to have accessed.
In the case of the gravel sources in the Gunnison Basin, we do not know at this
point in our research program which quartzite formation or formations served as
the ultimate geologic source for the gravels. Unfortunately, detailed geologic maps
have been completed for only a few isolated localities in the Gunnison Basin.
Figure 1. Location of the Chance Gulch site and six sampled quarries.
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Figure 2. (a) Source 1, Tertiary gravel deposit; (b) Source 2, Precambrian quartzite outcrop. The sources
are located about 0.5 km north and northeast respectively of the Chance Gulch site.
We have ascertained from them and from more generalized geologic maps that
quartzite-bearing units in the Gunnison Basin include Precambrian metaquartzite
and quartz veins, Paleozoic and Mesozoic orthoquartzite, Paleozoic and Mesozoic
quartzite-bearing conglomerate, Paleozoic and Mesozoic contact-metamorphosed
sandstone, and Tertiary and Quaternary quartzite-bearing gravel (DeWitt, Stoneman,
& Clark, 1985; Hedlund & Olson, 1974; Hunter, 1925; Olson, 1976a; Streufert, 1999;
Tweto, 1987; Zech, 1988). These geologic sources are summarized in Table I, and a
crucial component of our future field research will be to determine which formation(s), if any, were the source(s) of the Tertiary gravels we sampled.
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750
Fossil Ridge
Widespread
Cambrian Saguache Quartzite
Ordovician Harding Sandstone
Ordovician Parting Sandstone
Jurassic Junction Creek Sandstone
Cretaceous Dakota Formation
Orthoquartzite
Orthoquartzite
Metaquartzite and
bull quartz
Type of Quartzite
Potential outcrop
Potential outcrop
Potential outcrop
Source Type
DeWitt et al., 2002; DeWitt,
Stoneman, & Clark, 1985; Streufert,
1999; Zech, 1988
DeWitt, Stoneman, & Clark, 1985;
DeWitt et al., 2002; Streufert, 1999;
Zech, 1988
DeWitt, Stoneman, & Clark, 1985;
Hedland & Olson, 1974; Olson, 1976a,
1976b; Zech, 1988
References
Eastern part of study
area
Widespread, confined
mostly to drainages
Quaternary gravels
Fossil Ridge
Widespread
Tertiary gravels
Ordovician Harding Sandstone
Ordovician Molas Formation
Cambrian Peerless Formation
Cretaceous Dakota Formation
Cretaceous Mesa Verde Formation
Jurassic Morrison Formation
Cretaceous Burro Canyon Formation
GEOARCHAEOLOGY: AN INTERNATIONAL JOURNAL, VOL. 23, NO. 6
Quartzite clasts in
unconsolidated sediment
Quartzite clasts in
unconsolidated sediment
Contact metamorphosed
orthoquartzite
Quartzite–clast
conglomerate
Potential cobble
Potential cobble
Potential outcrop
Potential outcrop
Streufert, 1999
DeWitt, Stoneman, & Clark, 1985;
DeWitt et al., 2002; Gaskill, DeLong,
& Cochran, 1987; Olson, 1976a;
Streufert, 1999; Zech, 1988
DeWitt et al., 2002; DeWitt,
Stoneman, & Clark, 1985; Zech, 1988
Gaskill, 1977; Gaskill et al. 1986;
Gaskill, DeLong, & Cochran, 1987;
Hedland & Olson, 1974; Olson, 1976a;
Streufer,t 1999
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Headwaters Chance
Gulch, Fossil Ridge
Precambrian metaquartzite
and quartz veins
Location
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Quartzite-Bearing Units
Table I. Geologically mapped quartzite-bearing units in the Gunnison Basin, Colorado.
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For purposes of this methodological study, however, our goal was, again, to sample geologic sources of quartzite near the Chance Gulch site as well as sources farther
afield and unlikely to have been quarried by Chance Gulch occupants. In so doing, we
endeavored to build into our sample assemblage potential for geologic samples both
to vary from sample to sample (e.g., those located very far apart and the Tertiary gravels vs. the Precambrian outcrop located close to one another) and in other cases to show
similarities to each other (within, for example, a single gravel source; within the subassemblage of eight flakes; or from artifact to nearby geologic source of quartzite).
Future studies will necessarily sample a far larger Gunnison Basin quartzite universe.
In the field, we collected twenty quartzite cobbles from each of the gravel sources,
expressly trying to maximize visual variability in cobble colors and textures. We also
collected five samples from the Precambrian outcrop source just north of the Chance
Gulch site and from the UMG outcrops in northeastern Utah. In the laboratory, practical considerations required that we subsample further: We could not afford on our
pilot-study budget to subject each of the more than 100 total samples we had collected
to the six different and in some cases expensive analytical techniques we wished to
explore. We thus followed the protocol below to select an assemblage of twenty
quartzite specimens (plus two replicates—duplicates of the same sample—to serve
as experimental controls).
First, we assembled the eight large Chance Gulch flakes targeted for analysis on
our laboratory table. We then selected, from the prehistorically quarried sources
located near Chance Gulch (Sources 1, 3, and 4), eight geologic samples that, in
terms of color and texture, approximated those flakes. Those specimens represented
quartzites that we have hypothesized Chance Gulch knappers used (e.g., Pitblado,
2002). Next, we added to our assemblage one sample of the nearby Precambrian
outcrop source (Source 2), one from the Montrose gravel deposit (Source 5), and two
from Uinta Mountain Group outcrops (UMG-1 and UMG-2), all specimens one would
not reasonably expect—based on their geography (Sources 5 and UMG-1 and -2) or
non-conchoidal fracture properties (Source 2)—to represent the geologic origin
of the Chance Gulch artifacts. Two specimens, one from gravel Source 3 and one
from Montrose Source 5, served as replicates in our geochemical studies. Table II presents an overviews of all twenty samples and identifies the two replicates. Figure 3
shows examples of the geologic and culturally modified samples with which we
experimented.
Analytical Procedures
After we compiled our study assemblage, we identified the analytical techniques
to which we would subject the samples to develop profiles that could be compared
and contrasted: UVF, petrography, WD-XRF, INAA, AD-ICP-MS, and LA-ICP-MS. As
previously discussed, these techniques are well established in archaeological and
other literature as viable methods for sourcing a broad range of materials, so we do
not devote space here to overviews of general technique mechanics. These are summarized, however, in Pitblado et al. (2006b) and discussed in many venues (e.g.,
Goffer, 1980; Herz & Garrison. 1998; Newsome & Modreski, 1981; Odell, 2000, 2004;
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Table II. All quartzite samples subjected to sourcing experimentation.
Sample ID
A1-28
AC-23
B0-115
C2-72
C1-214
C9-17
D4-26
G8-11
1-7
1-15
2-1
3-6
3-6a
3-8
4-3
4-13
4-14
4-19
5-1
5-1a
UMG-1
UMG-2
Source
Chance Gulch
Chance Gulch
Chance Gulch
Chance Gulch
Chance Gulch
Chance Gulch
Chance Gulch
Chance Gulch
Source 1, cobble 7
Source 1, cobble 15
Source 2
Source 3, cobble 6
Source 3, cobble 6
Source 3, cobble 8
Source 4, cobble 3
Source 4, cobble 13
Source 4, cobble 14
Source 4, cobble 19
Source 5, cobble 1
Source 5, cobble 1
UMG source
UMG source
Source Type
Archaeological site
Archaeological site
Archaeological site
Archaeological site
Archaeological site
Archaeological site
Archaeological site
Archaeological site
Gravel source
Gravel source
Outcrop
Gravel source
Replicate 3-6 sample
Gravel source
Gravel source
Gravel source
Gravel source
Gravel source
Montrose Gravel source
Replicate 5-1 sample
Outcrop
Outcrop
County
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Gunnison, CO
Montrose, CO
Montrose, CO
Summit, UT
Summit, UT
Orna & Lambert, 1996; Pollard & Heron, 1996; Shackley, 1998; Speakman & Neff,
2005; Tykot, 2004).
We do offer an overview of the laboratories, equipment, and protocols used to
conduct each form of analysis. Coauthor Pitblado oversaw UVF in her laboratory on
the Utah State University (USU) campus. Prior to crushing and under darkroom conditions, two research assistants subjected quartzite samples first to short-wave and
then to long-wave ultraviolet light. They immediately recorded their observations,
which included subjective descriptions of color (including modifiers such as “bright”
or “pale”) and texture (e.g., some samples looked “velvety” when fluoresced).
Although descriptions were necessarily subjective, this component of the analysis
was conducted first by one research assistant and then independently by the second.
Pitblado compiled the two sets of observations, which matched well, to develop the
final database.
Coauthor Dehler oversaw the petrographic analysis of our samples. First, two
research assistants cut the gravel and outcrop samples into billets (12 24 46
mm) using rock saws in the Geology Department at USU. Quality Thin Sections of
Tucson, Arizona, made thin sections from these billets, hand polishing the final products to 30 microns thick. Geology graduate student Caroline Myer thoroughly
described each thin section in terms of texture (grain/crystal size, sorting, rounding), composition of grains or crystals, cement composition, bedding or foliation,
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Figure 3. Examples of geologic and artifact samples analyzed in the research: (a) left, Source 1-7; right,
artifact C9-17; (b) left, Source 3-8; right, artifact A1-28; (c) left, Source 4-13; right, artifact D4-26; (d)
Source 1-15 (shown through petrography to be an altered volcanic tuff, rather than the quartzite it appeared
macroscopically to be).
secondary features (e.g., fractures), and crosscutting relationships. She and coauthor
Dehler established categories, and then Myer performed a 300-point point count on
each thin section. Myer took photomicrographs for each thin section for sample
characterization. To conduct the petrographic analysis, Myer used an Olympus BH2
microscope with a Canon Powershot G6 and point-count mechanisms attached.
Coauthor Steve Nelson and Dave Tingey (Brigham Young University Department
of Geological Sciences) conducted WD-XRF analysis of our quartzite samples in
crushed form. They analyzed a selected suite of elements on a Siemens (Brucker) SRS
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303 instrument employing an Rh end-window X-ray tube. They made all measurements on pressed powder pellets rather than glass to increase count rates for the
elements of interest. Uncertainties for all elements above detection limit for
the WD-XRF analysis are 5–10%.
Coauthor Dehler and Scott Hughes (Idaho State University) conducted INAA
analysis. They powdered INAA splits to 100 mesh size in an alumina ceramic shatterbox. Aliquots of samples (~0.7 g each, weighed to the nearest 0.0001 g) along with
standards (e.g., NIST SRM1633b, BCR-1, BHVO-1, and in-house standards) were irradiated for two hours in the Oregon State University TRIGA reactor under a neutron
flux of 3 1012 n · cm2 · s1. Lab personnel made sequential gamma ray counts over
an 80–2000 keV spectral range using two high-purity Ge detectors and a multichannel
analyzer calibrated at 0.22 keV/ch. Hughes and colleagues conducted three analyses
(after 5–6 days, 8–14 days, and 20 days of decay following irradiation) to optimize
precision for short-, intermediate-, and long-lived radionuclides of Fe, Na, Sc, Cr,
Co, Ni, Zn, Rb, Sr, Cs, Ba, La, Ce, Nd, Sm, Eu, Tb, Yb, Lu, Zr, Hf, Ta, Th, and U.
Analytical uncertainties are 2% for all major elements and trace elements Sr, Ba,
and Zr. Uncertainties for all other trace elements are 1–2% for Sc, Co, La, Sm, and Eu;
2–4% for Cr, Ce, Yb, Hf, and Ta; 5–10% for Rb, Cs, Nd, Tb, Lu, and Th; and 10% for
Ni, Zn, and U.
The commercial laboratory ALS Chemex conducted AD-ICP-MS analysis of our
samples. Lab personnel analyzed the rare earth elements (REE), U, Th, and Y by
fusion at 1000°C of 0.2 g of sample digested in 0.9 g of lithium metaborate flux. They
then cooled and digested the resulting mixture in 100 mL of 4% HNO3. For all other
elements, they prepared samples by acid digestions of 0.25 g of sample in HClO4,
HNO3, and HF. Residues were taken up in a dilute HCl solution. Chemex personnel
then analyzed all samples and standards (SY-4 and two internal standards) with a
PerkinElmer Elan 9000 ICP-MS, with appropriate corrections for inter-element interferences. The lab report indicates maximum acceptable error of 10% for both duplicate tolerance and internal standards.
The Institute for Integrative Research in Materials, Environments, and Society
(IIRMES) archaeometry lab at California State University–Long Beach carried out
LA-ICP-MS analysis. Instrumentation consists of a New Wave UP-213 laser ablation
system coupled to a GBC Optimass 8000 time-of-flight (TOF) ICP-MS. Lab personnel set the laser at 60% power, with a 100-micron spot size, and ran the laser over a
small raster pattern at a rate of 50 microns per second. Following an initial preablation pass, signal intensities for 44 analytes (Na, Al, Si, K, Ca, Sc, Ti, V, Cr, Mn, Fe,
Co, Ni, Cu, Zn, As, Rb, Sr, Y, Zr, Nb, Sn, Sb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb,
Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Pb, Th, and U) were measured by the TOF four times
for 5 seconds each. IIRMES staff used NIST standard glasses SRM614 (approximately
0.7 ppm of most trace elements) and SRM612 (approximately 40 ppm of most trace
elements) together with Little Glass Buttes obsidian to calibrate average signal intensities to concentrations, with silicon (monitored at mass 30 to avoid saturation of the
detector) used as the internal standard. Detection limits calculated on the quartzite
runs for most elements (blank value plus three standard deviations of the blank)
were below 0.5 ppm for almost all trace elements, the exceptions being Sc, Cr, and
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Sr. RSDs on the SRM612 were below 5% for almost all trace elements, the main
exceptions being Mg, Th, Cr, and Ni.
PILOT STUDY RESULTS
On the basis of over 150 total observations, we drew conclusions about the utility
of each analytical strategy and developed interpretations of the data produced by
those that worked best: petrography, INAA, and both forms of ICP-MS. The two analytical techniques that our pilot study suggests cannot contribute meaningfully to characterization of Gunnison Basin quartzites are UVF and WD-XRF. Although UVF has been
used to assign microcrystalline and even occasionally quartzite artifacts to possible geological sources (e.g., Benedict, 1996; Cassells, 1995; Hofman, Todd, & Collins, 1991;
Lyons, Glascock, & Mehringer, 2003; Pitblado, 2000), our samples failed in all but two
cases to fluoresce at all when exposed to short- or long-wave ultraviolet light.
Our WD-XRF analysis, complete results of which can be accessed at https://www.
paleostrat.org/Public/projects.aspx?pide2bf1281-b96a-48ad-85e3-9b2a8df29b6a,
likewise showed no ability to discriminate among our quartzites. This outcome is
rooted in the fact that any geochemical technique will effectively profile quartzite only
to the extent that it can detect and measure the often 2% of a quartzite sample that
is not quartz. Our XRF analysis detected 16 elements-18% of the 90 naturally occurring elements in the periodic table (Table 3). Of those, some failed to register in a specimen or registered in amounts close to XRF detection limits, yielding suspect results.
Based on the inability of our XRF analysis to detect a broad spectrum of trace elements and the probability (borne out by other tests) that our samples do not vary on
the basis of the 18% of elements detected, we have ruled out XRF for discriminating
among Gunnison Basin quartzites.
On the other hand, our petrographic analysis and three geochemical techniques
(INAA, AD-ICP-MS, and LA-ICP-MS) did yield results that tentatively show significant variability among Gunnison Basin quartzites, a requisite for sourcing. In the remainder of this
paper, we discuss the results of these four forms of analysis, beginning with petrography and segueing into our geochemical tests, clarifying in so doing why we believe the
greatest potential for sourcing quartzite and specifically artifacts made of quartzite may
lie in an approach that integrates petrography and minimally destructive LA-ICP-MS.
Petrography
Cobble and Outcrop Samples
Composition. Petrographic results show variability in composition of samples
within and between cobble and outcrop sample sets (Table IV, Figure 4). Grain compositions of all samples are quartz-rich, yet samples vary in other mineral components,
namely plagioclase and muscovite. Cement composition is dominated by silica, although
of varying percentages; other cements include clay minerals and hematite.
Cobble samples vary in composition and show much promise for identifying distinctive populations. A sample subset from Sources 1, 3, and 4 shows small percentages
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Table III. Elements detected in samples through LA-ICP-MS, AD-ICP-MS, INAA, and WD-XRF (and
detection levels for each element for each technique).
Element Detection by Analytical Technique (Detection Levels in Parentheses)
Element
(Units)
Ag (ppm)
Al (%)
As (ppm)
Ba (ppm)
Be (ppm)
Bi (ppm)
Ca (%)
Cd (ppm)
Ce (ppm)
Co (ppm)
Cr (ppm)
Cs (ppm)
Cu (ppm)
Dy (ppm)
Er (ppm)
Eu (ppm)
Fe (%)
Ga (ppm)
Gd (ppm)
Ge (ppm)
Hf (ppm)
Ho (ppm)
In (ppm)
K (%)
La (ppm)
Li (ppm)
Lu (ppm)
Mg (%)
Mn (ppm)
Mo (ppm)
Na (%)
Nb (ppm)
Nd (ppm)
Ni (ppm)
P (ppm)
Pb (ppm)
Pr (ppm)
Rb (ppm)
Re (ppm)
S (%)
Sb (ppm)
756
LA-ICP-MS*
X (0.002)
X (0.18)
X (0.02)
X (0.003)
X (0.61)
X (0.05)
X (0.88)
X (0.004)
X (0.13)
X (0.30)
X (0.36)
X (0.09)
X (0.001)
X (0.28)
X (0.47)
X (0.04)
X (0.05)
X (0.38)
X (0.15)
X (0.08)
X (0.001)
X (0.02)
X (0.01)
X (0.08)
X (0.35)
X (0.39)
X (0.01)
X (0.12)
X (0.02)
X (0.02)
AD-ICP-MS
X (0.01)
X (0.01)
X (0.2)
X (10.0)
X (0.05)
X (0.08)
X (0.02)
X (0.02)
X (0.01)
X (0.1)
X (1.0)
X (0.05)
X (0.2)
X (0.1)
X (0.1)
X (0.1)
X (0.01)
X (0.05)
X (0.1)
X (0.05)
X (0.1)
X (0.1)
X (0.005)
X (0.01)
X (0.5)
X (0.2)
X (0.1)
X (0.01)
X (5.0)
X (0.05)
X (0.01)
X (0.1)
X (0.5)
X (0.2)
X (10.0)
X (0.5)
X (0.1)
X (0.1)
X (0.002)
X (0.01)
X (0.05)
INAA
WD-XRF
X (0.005)
X (0.6)
X (6.0)
X (0.007)
X (0.03)
X (0.006)
X (0.05)
X (0.01)
X (0.001)
X (0.002)
X (0.007)
X (0.01)
X (0.008)
X (0.008)
X (0.03)
X (0.001)
X (0.006)
X (80.0)
X (0.001)
X (0.007)
X (4.0)
X (0.2)
X (40.0)
X (0.2)
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X (1.0)
(Continued)
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Element Detection by Analytical Technique (Detection Levels in Parentheses)
Element
(Units)
Sc (ppm)
Se (ppm)
Sm (ppm)
Sn (ppm)
Sr (ppm)
Ta (ppm)
Tb (ppm)
Te (ppm)
Th (ppm)
Ti (%)
Tl (ppm)
Tm (ppm)
U (ppm)
V (ppm)
W (ppm)
Y (ppm)
Yb (ppm)
Zn (ppm)
Zr (ppm)
LA-ICP-MS*
X (1.32)
X (0.32)
X (0.04)
X (1.86)
X (0.07)
X (0.04)
X (0.0003)
X (0.08)
X (0.003)
X (0.15)
X (0.13)
X (0.49)
X (0.27)
X (0.27)
AD-ICP-MS
X (0.1)
X (1.0)
X (0.1)
X (0.2)
X (0.2)
X (0.05)
X (0.1)
X (0.05)
X (0.2)
X (0.005)
X (0.02)
X (0.1)
X (0.1)
X (1.0)
X (0.1)
X (0.1)
X (0.1)
X (2.0)
X (0.5)
INAA
WD-XRF
X (0.001)
X (0.003)
X (0.01)
X (0.002)
X (0.002)
X (0.01)
X (0.006)
X (0.1)
X (1.0)
X (0.008)
X (3.0)
X (1.0)
X (1.0)
* In this preliminary study, a number of elements included in the AD-ICP-MS analyses were not included
in the method used for LA-ICP-MS; omission from the table therefore does not necessarily indicate that
the elements are below detection limits of LA-ICP-MS.
Complete raw data sets for all forms of geochemical analysis are accessible at the online databank,
https://www.paleostrat.org/Public/projects.aspx?pide2bf1281-b96a-48ad-85e3-9b2a8df29b6a.
of plagioclase grains (0.4–2.0%). A sample subset from Sources 3, 4, and 5 contain
entirely quartz grains. Clay cement is a small component in a sample subset from all
sources but Source 5. A subpopulation of the quartz grains in gravel sample 5-1 are
composed of chert, making this a unique sample in the assemblage. The “cement” in
sample 1-15 is actually the groundmass of an altered tuff and is a mixture of feldspar
and quartz. There is sufficient variety in the compositional types represented in the
assemblage, whether due to “cement” or grain types, that geochemical differentiation of these samples should be expected (and as we will later show, occurs).
The outcrop samples in the assemblage (n 3) all vary in composition and should
(and do) produce unique geochemical signatures. Sample 2-1, proximal to gravel
Sources 1, 3, and 4, shows the highest percentage of plagioclase (3.2%). UMG-1 contains nothing but quartz grains. UMG-2 also contains plagioclase grains (1.6%) and
a very high percentage of detrital muscovite (14.4%).
There are some strong matches between cobble and outcrop sample compositions. Outcrop sample 2-1 matches only one cobble sample, 1-15, which, it turns out
(contrary to its macroscopic appearance), is a volcanic rock (altered tuff) (Figures
3d, 4a). If geochemical analyses were done on these two samples in the absence of
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Figure 4. Photomicrographs of cobble, outcrop, and artifact samples in the pilot study: (a) gravel sample
1-15 and outcrop sample 2-1 showing textural differences and compositional similarities (texture in cobble sample 1-15 is porphyritic and texture in 2-1 is poorly sorted); (b) gravel sample 3-8, artifact sample
B0-115, and outcrop sample UMG-1 showing compositional similarity and differing textures; (c) outcrop
sample UMG-2 and gravel sample 5-1 showing textural and compositional differences; (d) gravel sample
4-3 and artifact sample C9-17 showing textural similarities and compositional differences. Q quartz,
P plagioclase, M mica, G groundmass (volcanic texture), H hematite cement, Ma matrix.
cobble
cobble
cobble
cobble
cobble
cobble
cobble
cobble
cobble
artifact
artifact
artifact
artifact
artifact
artifact
artifact
artifact
outcrop
outcrop
outcrop
1-15
1-7
3-6
3-8
4-13
4-14
4-19
4-3
5-1
C2-72
C9-17
B0-115
A1-28
D4-26
A0-23
G8-11
C1-214
UMG-1
UMG-2
2-1
altered tuff
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
orthoquartzite
Rock type
56.80%
99.20%
92.40%
84.00%
89.80%
93.60%
96.40%
98.40%
87.60%
98.00%
96.40%
98.80%
98.00%
88.40%
99.20%
91.60%
93.60%
98.80%
77.60%
61.40%
Quartz
41.2% (g)
0.40%
7.60%
15.20%
1.20%
6.00%
3.60%
1.60%
12.40%
2.00%
3.60%
1.20%
1.60%
11.60%
0.80%
8.40%
3.20%
1.20%
6.40%
35.4% (m, c)
Cement/matrix/
groundmass
2.00%
0.40%
Plagioclase
Muscovite
14.40%
1.60%
3.20%
(Continued)
2.40%
0.40%
0.80%
0.40%
0.80%
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Sample type
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Sample #
Table IV. Results of petrographic characterization of pilot study quartzite assemblage.
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Quartz/feldspar
Silica/clay
Silica/clay
Silica/clay
Silica /clay
Silica
Silica /clay
Silica
Silica
Silica /clay
Hematite/silica /
clay
Silica /clay
Silica /clay
Silica /clay
Silica
Silica /clay
Silica/clay
Silica
Silica
Silica/sericite
1-15
1-7
3-6
3-8
4-13
4-14
4-19
4-3
5-1
C2-72
C9-17
Phenocrysts
Angular-rounded
Subrounded
Angular-rounded
Subangular-rounded
Subangularsubrectangular
Angular-rounded
Angular-subangular
Angular-rounded
Subrounded
Subrounded
Roundness
Moderate
Well
Poor
Well
Poor
N/A
Moderate
Well
Moderate–poor
Well
Well
Sorting
Crosslamination, pseudom
Crossbedding
Silicified volcanic
Graded bedding
Other Comments
B0-115
A1-28
D4-26
A0-23
G8-11
C1-214
UMG-1
UMG-2
2-1
Subangular-rounded
Subangular
Subrounded
Subangular-subrounded
Subangular-rounded
Agular-subrounded
Subangular- subrounded
Angular-rounded
Angular
Fine-medium
Fine–medium
Silt–medium
Fine–coarse
Fine–medium
Fine–coarse
Medium–coarse
Fine–medium
Silt–coarse
Moderate
Poor
Moderate–poor
Poor
Moderate
Moderate–poor
Moderate
Moderate–well
Poor
Cross-lamination, sub wacke
Subarkosic wacke
Chert grains, crossbedding
Crosslamination
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Fine to medium
Coarse
Silt–coarse
Medium–coarse
Medium–coarse
Fine to coarse
Fine–medium
Coarse
Medium–coarse
Fine to medium
Medium–coarse
Grain/
Crystal Size
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Cement/matrix/
Groundmass
Sample #
Table IV. (Continued).
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petrography, they would likely be interpreted as the same rock type and a good
match, when in reality they have distinctly different geologic origins (we return to
this point in later discussions of geochemical results). UMG-1 matches cobble samples from Sources 3, 4, and 5 that do not contain plagioclase (e.g., Figure 4b). UMG-2
is unique when compared to every other sample because of its high detrital muscovite component (Figure 4c). There are no outcrop samples that match the
plagioclase-bearing samples in Sources 1, 3, and 4.
Texture. There is a variety of textures in both the gravel and outcrop samples,
some sufficiently unique to be distinguishable from other samples in the small pilot
population. Grain sizes range from silt to coarse sand, sorting ranges from well to
poor, and grains are angular to well rounded. It appears that sorting is a textural
characteristic with great promise for differentiating among quartzite from different
geological sources.
Textures in the cobble samples are quite variable and yet several distinct populations can be discerned. Grain sizes range from fine- to coarse-grained, grain shape
ranges from angular to rounded, and grain sorting is well to poor. One sample, 1-15
(Figure 4a), has a volcanic porphyritic texture, as opposed to a sedimentary one.
This can be discerned by the lack of coarse components with a detrital appearance
(evidence of transport). Rather, this sample shows well- developed plagioclase crystals that show no sign of transport (i.e., phenocrysts). Additionally, the groundmass
surrounding these phenocrysts does not have a clastic texture. Although this sample has some of the same qualities as a true orthoquartzite (e.g., conchoidal fracture, composition), it falls under a unique category (i.e., not a microcrystalline either)
and is simply a silicified tuff. Sample 5-1 appears to have better rounding than the
rest of the cobble samples and contains chert grains not apparent in other samples.
Sample 3-8 is more poorly sorted than any other cobble exhibiting sedimentary textures (compare Figures 4b and 4c). The rest of the samples have textural characteristics that are difficult to group into more distinct populations.
Textures of the outcrop samples are strikingly different from one another. They
show a range of grain sizes, rounding, and sorting. The sorting is a characteristic
that allows, as with the cobble samples, separation into distinct populations. The
poor sorting of outcrop sample 2-1 (Figure 4a) distinguishes it from the other two
outcrop samples (Figures 4b, 4c). Sample UMG-2 is the only outcrop sample to
exhibit a dominantly fine grain size in combination with a high degree of sorting.
A comparison of textures from cobble and outcrop samples shows some discernible trends. The poor sorting in outcrop sample 2-1 is also reflected in the volcanic gravel sample 1-15 and the orthoquartzite gravel sample 3-8 (Figure 4a). Outcrop
sample UMG-2 shows a combination of sorting and grain size not seen in any
other sample. Outcrop sample UMG-texturally matches many of the gravel samples
(despite the fact that the former originated in northeastern Utah and the latter in
the Gunnison Basin, Colorado).
Summary. There is enough variety among the samples to develop distinct populations and, in some cases, single out a unique sample that may be especially useful
for sourcing studies. Although there are some good compositional and textural trends
alone, the most robust approach is to use composition and texture combined to identify
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populations. An overall comparison of the cobble samples with the outcrop samples
shows that all but one of the cobble samples reflects a compositional distinction from
the local outcrop sample 2-1. The remaining cobble sample (1-15) closely matches outcrop sample 2-1 in composition, yet it has a volcanic texture. If only geochemical
analyses were performed on these two samples, a genetic distinction could go unnoticed, a scenario that emerged in our analysis of AD-ICP-MS data and which we will
discuss in that section of the paper. Sample 3-8, although compositionally different,
has very similar sorting properties to outcrop sample 2-1.
One of the Utah outcrop samples (UMG-2) is compositionally and texturally unique
from all other samples, as we would predict it would be. The other (UMG-1) does,
however, match the composition of the average cobble sample composition and texture, suggesting that attempts to geochemically differentiate it from Gunnison Basin
sources are advised. Finally, cobble sample 5-1 exhibits chert grains not seen in any
other sample and is probably a different source entirely (a conclusion that is, archaeologically speaking, highly likely because 5-1 derives from a location well removed
from the Gunnison Basin). Again, however, in theory this difference may not be
detected in geochemical analyses alone, and so it may be the petrographic data that
make the difference discernible.
Artifacts
Petrographic results from artifacts show fairly homogeneous composition and a
range of textures, yet some populations are still distinguishable and certain cobble and
outcrop sources can be eliminated as raw material sources for those artifacts (Table IV).
Although all eight artifacts examined in thin section are dominated by quartz grains
(88–99.2%), one sample (A1-128) has minor muscovite (0.4%), and another (C1-214)
has minor muscovite (2.4%) and plagioclase (0.8%). Artifact C9-17 has a distinctive
hematite cement not seen elsewhere in the greater sample set (Figure 4d). The remaining artifact samples match compositions of samples from all gravel sources and do
not match that of the local outcrop sample (2-1) nor one of the two northeastern Utah
outcrop samples (UMG-2) (yet they do match well with UMG-1). The two micaceous
artifact samples are both also poorly sorted, making them a unique population among
the artifacts. Otherwise, textures of the artifact samples show a range in grain sizes,
sorting, and roundness, and there does not appear to be any trend or groupings in terms
of these characteristics. The artifacts do seem to represent the average orthoquartzite
sample in the pilot study both texturally and compositionally, and it may be that prehistoric knappers preferentially selected the most mature orthoquartzites for tool
production (a proposition to test in the future).
Geochemical Analysis: INAA, AD-ICP-MS, and LA-ICP-MS
Both INAA and AD-ICP-MS yield accurate and precise measurements of trace elements in samples of various kinds, and both measure element abundances in the
low parts per million (ppm). INAA of our quartzite samples detected 22 elements
(Table III). Although this is still just 24% of the 90-element periodic table versus 18%
for XRF, the suites of elements detected by the two methods share only 5 in common.
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That is, INAA detected 17 elements that XRF did not. Our AD-ICP-MS analysis
detected 60 elements in our samples—67% of naturally occurring elements and triple
the elements detected by XRF or INAA (Table III). AD-ICP-MS detected all 22 elements
detected by INAA, all 16 detected by XRF, plus another 27 trace elements. In terms
of sensitivity to the presence of various elements, AD-ICP-MS outperformed INAA.
Complete raw data sets for both can be accessed at https://www.paleostrat.
org/Public/projects.aspx?pide2bf1281-b96a-48ad-85e3-9b2a8df29b6a.
As a first step in our assessment of the data obtained prior to the introduction of
the LA-ICP-MS analytical technique to the pilot study, we compared the quality of our
AD-ICP-MS and INAA data, because the latter is still considered by many archaeologists to be something of a gold standard in sourcing analysis. To do this, we conducted a linear regression of 19 of the 22 total elements reported in both analyses,
excluding Co, Hf, and Zr because initial evaluation of the raw data showed that
reported abundances were elevated by factors of 3.5, 6.8, and 7.0, respectively, for
the INAA relative to AD-ICP-MS data. For Zr and Hf, this was undoubtedly due to the
incomplete digestion of zircon (ZrSiO4), which contains both elements, by acid leaching during AD-ICP-MS sample preparation, a problem that could perhaps be remedied in future studies through additional sample preparation by fusion in lithium
metaborate flux.
We conducted linear regression analysis of the INAA and AD-ICP-MS data for the
REE, the REE plus U and Th, and for all 19 elements collectively on a sample-bysample basis. In principle, regression analysis should produce a slope of 1, an R2 value
of 1, and a y-intercept of 0 if the results are identical for both techniques. It is apparent that INAA and AD-ICP-MS do yield statistically indistinguishable results for the
REE and the REE plus U and Th, but not when all 19 elements are considered together
(Table V). In our analysis, we treated INAA data as the independent variable. Thus, a
mean slope of 0.88 indicates that at least some of the elements other than the REE,
U, and Th have systematically higher values when detected by INAA than when
detected by AD-ICP-MS. Further scrutiny of our data suggests these elements include
Ba, Cr, Ta, and Tb. In this regard, we note that a relatively abundant trace element like
Ba may have exerted considerable leverage in our regression analysis. In fact, deleting Ba from consideration increased the mean slope from 0.88 to near unity (0.96).
In general, our linear regression analysis reveals excellent agreement among INAA
and AD-ICP-MS data for our quartzite specimens, with only a few discrepancies that
we can explain and that could be compensated for in future research (for example,
by refining AD-ICP-MS preparatory methods). For other discussions of the comparability of INAA and AD-ICP-MS and the often excellent performance of AD-ICP-MS
Table V. Results of linear regression of INAA and AD-ICP-MS data (19 of 22 shared elements) for all
geologic and artifact samples.
Mean slope
Mean R 2
Mean y-intercept
REE Only
REE, Th, U
19 Common Elements
1.01 0.11
0.99 .01
0.09 0.25
1.01 0.10
0.98 0.02
0.01 0.20
0.88 0.13
0.99 0.02
0.43 0.43
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analysis, we refer readers to Mallory-Greenough, Greenough, and Owen (1998) and
Pollard and Heron (1996). Because AD-ICP-MS analysis is less expensive than INAA,
can be performed more quickly than INAA, is no more destructive than INAA, does
not yield radioactive samples as byproducts as does INAA, and is significantly more
sensitive to the trace elements present in our pilot-study samples, we emerged from
this phase of analysis concluding that AD-ICP-MS holds more promise for our future
experiments in quartzite sourcing through geochemical means than does INAA.
We therefore proceeded to look more closely at the AD-ICP-MS data to determine
what preliminary conclusions we might draw about the potential of this geochemical technique to discriminate among geologic and archaeological quartzite samples.
Table VI shows the mean concentration of the REE in our specimens as detected by
AD-ICP-MS. The variability in the abundances of these elements is of a similar order
to their mean concentrations, a clear indication that the pilot-study quartzites are neither “barren” nor geochemically monotonous. Rather, the large differences signify
inherently excellent potential for discriminating among geologic sources of quartzite
and between artifacts and their geologic sources.
Figure 5, which plots the mean concentration of 30 elements detected by AD-ICPMS by the mean value in pegmatic quartz, reinforces this point, illustrating visually
the enrichments in trace elements above and beyond what we might expect of pure
quartz. The simple conclusion to be drawn (but one that runs contrary to many earth
scientists’ perceptions of quartzite) is that our quartzites harbor many trace elements. This condition is a requisite for large-scale discrimination among geologic
Figure 5. Mean concentration of elements detected by AD-ICP-MS divided by the mean value in pegmatite quartz, revealing the enrichment in trace elements in the sample assemblage compared to pure
quartz.
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Table VI. Representative REE concentrations for all artifact and geological
samples, as determined by AD-ICP-MS.
Element
La
Ce
Pr
Nd
Sm
Eu
Gd
Tb
Dy
Ho
Er
Tm
Yb
Lu
Mean Concentration (ppm)
9.86
22.28
2.34
8.99
1.87
0.36
1.92
0.34
1.88
0.40
1.28
0.26
1.28
0.22
1
Count
10.63
28.20
2.97
11.63
2.41
0.43
2.33
0.40
2.21
0.48
1.42
0.24
1.39
0.23
22
22
22
22
22
17a
22
19a
22
21
22
13a
22
18a
a
Although 22 samples were analyzed (duplicates of two cobble sources were
submitted as data controls), some samples had concentrations below detection limits.
sources of quartzite, and it is a requisite for future success in statistically matching
quartzite artifacts to likely geologic sources on the landscape. Moreover, both
Table VI and Figure 5 confirm that technology has reached the point where we can
routinely detect trace element patterns in these kinds of rocks, an observation evidently not yet embraced by earth scientists, given the virtual nonexistence of geochemical studies of quartzite composition.
To gain a preliminary sense of the structure of the AD-ICP-MS data as it pertains
to discrimination among sources and samples, we performed an average-linkage
cluster analysis based on mean Euclidean distances (Figure 6). This data exploration
took into account complete trace-element profiles of our samples, rather than a subset of elements (as, for example, is the case with the bivariate element plots presented later in this paper). Although the sample size used to produce the graph
(n 22) is small, the results yield insights into the discriminatory potential of
AD-ICP-MS. For instance, and gratifyingly, replicate samples 5-1 and 5-1a and 3-6
and 3-6a show the closest relationships of any sample couplets. In addition, the two
specimens from Source 3 (3-6 and 3-8) cluster tightly together, as we would hypothesize samples from the same source would do. On the other hand, and as previously
discussed, petrographic data show that the Source 3 samples differ texturally from
one another (see Table IV), an important reminder that petrographic analysis can yield
data and insights that geochemical techniques may mask.
In the cluster diagram, the two geologic specimens from Source 1 (1-7 and 1-15),
which, like Source 3, is located within 0.5 km of the Chance Gulch site, show a reasonably close association both with each other and with the Source 3 cobble samples. This result is expected, because Sources 1 and 3 are cobble deposits that we
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Figure 6. Average-linkage cluster analysis based on mean Euclidean distances (AD-ICP-MS data). All
geologic and archaeological samples have been included in the cluster analysis. Samples C9-17, D4-26,
G8-11, B0-115, A1-28, C2-72, AC-23, and C1-214 are quartzite artifacts from the Chance Gulch site. All
other samples are Gunnison Basin sourcing project quarries. Samples 5-1 and 5-1a are replicate samples,
as are 3-6 and 3-6A.
hypothesize originated from parent sources upstream (a proposition to be tested
in future research). Here again, however, we point out that petrographic data reveal
a distinction not suggested in the AD-ICP-MS data set (at least not in the cluster
analysis): The two Source 1 samples differ texturally from one another, and rather
dramatically. Sample 1-15, contrary to its macroscopic appearance (Figure 3d) and
to all other samples in our assemblage (Figure 3d, Table IV), is altered tuff. Sample
1-7 is orthoquartzite.
The Chance Gulch artifacts we tested cannot yet be associated with known geologic sources of quartzite; that sort of conclusion can only come with much more intraand inter-source profiling of geologic sources in the region. However, we note that
the cluster diagram suggests markedly similar holistic geochemical profiles on the
part of Chance Gulch artifacts A1-28, C2-72, and AC-23. We further note that these
three artifacts in turn cluster with sample 1-7 from a nearby gravel source and slightly
more distantly with samples 1-15 and 2-1, also from nearby sources. Yet again petrographic data illuminate the situation, revealing that outcrop sample 2-1 differs
markedly both from the Source 1 and Source 3 geologic samples and from the three
artifacts. Samples 2-1 and 1-15 are almost identical mineralogically to one another
(hence their proximity in the cluster diagram), yet, as we have noted, 2-1 has a sedimentary texture and 1-15 a volcanic one. It is therefore unlikely both that the Source 1
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(and Source 3) cobbles derived from the outcrop that yielded sample 2-1 and that
Chance Gulch occupants made artifacts A1-28, C2-72, and AC-23 from Source 2
quartzite or cobbles like 1-15.
These observations and others (for example, that although artifact C1-214 and
outcrop sample UMG-2 cluster together in Figure 6, they differ texturally, likely indicating a spurious association) are important because they inform an inference we have
alluded to already and to which we will return in our conclusions. Future protocols
for fingerprinting Gunnison Basin quartzites should combine petrographic and geochemical analysis. Trace-element compositions can differentiate among quartzite
samples that look alike but which had different geneses. However, petrographic data
can—and in our data set do—differentiate among quartzite (and even one volcanic)
sample with holistically similar trace-element signatures.
We are not comfortable carrying our interpretations of the Figure 6 data further
than we already have. The sample size is too small and the data are too preliminary.
However, we emphasize in closing that we view AD-ICP-MS data as inherently capable of registering extensive variability in quartzite samples from the geologic sources
we sampled and in the quartzite artifacts from the Chance Gulch site. Moreover, the
preliminary look at the data afforded by the cluster analysis suggests that with more
samples and finer-grained multivariate investigation (most particularly, we anticipate, principal components analysis), geochemical discrimination among sources
will be possible. The appropriate question to guide future research may therefore be
not whether discrimination of Gunnison Basin quartzite sources can be achieved,
but rather, to what degree discrimination of those sources can be achieved.
However, there is one more important issue to be considered given our long-term
goal of sourcing Paleoindian-age artifacts. Regardless of how well AD-ICP-MS performed in our experiments and regardless of the technique’s apparently strong potential to discriminate among quartzite sources, the requirement that samples be crushed
to powder as part of the AD-ICP-MS sample-preparation process is problematic.
Crushing ubiquitous geologic specimens is acceptable, but we aim to eventually
match extremely rare quartzite artifacts to geologic sources to determine where the
Rocky Mountains’ most ancient residents obtained their stone raw materials and
how they used the landscape. Crushing Paleoindian spear points and other nonrenewable, humanly produced artifacts is undesirable. This reality underlay the next
step in our pilot study: introducing minimally invasive LA-ICP-MS analysis to
the equation.
Unfortunately, data generated through LA- and AD-ICP-MS analysis are not directly
comparable to one another. Therefore, as we look toward future large-scale sampling and characterization of quartzite localities that constitute the Gunnison Basin
sourcing universe, we cannot address the artifact-destruction issue by simply using
AD-ICP-MS to characterize geologic samples and non-destructive LA-ICP-MS analysis to profile artifacts. Rather, all samples must be analyzed using one method or
the other. Given the virtually non—destructive nature of LA-ICP-MS, it is clearly the
preferable choice-if, and this is a big “if”—it can discriminate among quartzite sources
as effectively or nearly as effectively as AD-ICP-MS.
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Past comparisons of the two techniques have shown that while AD-ICP-MS usually yields more accurate determinations of trace-element composition than LA-ICPMS, the two typically yield comparable distributions of the elements they detect in
common (e.g. Habicht-Mauche et al., 2002; Rockman, 2003). The obvious questions
for us therefore became whether LA-ICP-MS data generated from our quartzite samples would suggest an interpretive picture similar to those of our high-resolution
AD-ICP-MS data and whether the loss of elements not detected by LA-ICP-MS would
result in a significantly reduced ability to discriminate among our samples. With
these questions in mind, we undertook LA-ICP-MS analysis of our samples, although
we note that by this phase in our pilot-study research, our eight quartzite artifacts
had been expended to test other destructive techniques. We proceeded with our
twelve geologic samples and two replicates, and discussion henceforth pertains only
to them.
LA-ICP-MS detected 44 total elements in our quartzite samples (Table III). That is
16 fewer elements than the 60 detected by AD-ICP-MS; however, two caveats must
be stated. First, the sample size was smaller in our LA-ICP-MS analysis because we
tested only geologic specimens. Elements unique to any of our eight artifacts and not
present in our geologic samples could not have been detected by LA-ICP-MS. In addition, the IIRMES lab applied laser-ablation methods they developed for LA-ICP-MS
analysis of microcrystalline quartz to the quartzite testing, operating under the presumption that the constituent elements would be similar. This proved not to be the
case (the quartzite specimens are much “dirtier” than the microcrystalline samples
the IIRMES lab has analyzed for the past two years), and future methods will be
refined to maximize detection of all elements present. In other words, had we looked
for more elements in our LA-ICP-MS analysis of quartzite samples, we probably
would have found them.
To analyze the results of our LA-ICP-MS testing, we first compared the means of
the four LA-ICP-MS spot analyses to our AD-ICP-MS values for elements measured
in common. As Table VII shows, the results are less straightforward than those
obtained when we compared our AD-ICP-MS and INAA data, which overall show
strong consistency. The general explanation for the lack of perfect positive correlation is that (1) there is analytical error in both analyses at magnitudes discussed in
our methodology section; and (2) there are sampling issues that arise from the fact
that LA-ICP-MS targets a tiny area (in this case four tiny areas subsequently averaged),
whereas AD-ICP-MS results reflect a larger sample that has been powdered, digested,
and analyzed in bulk.
More specifically, the key to strong correlation is the probability that the laser
used in LA-ICP-MS will contact a trace element–abundant crystal in the sample.
Zircon, apatite, and sphene will have abundant REE, U, Th, and in the case of zircon,
Zr. However, they will be low in abundance and laser ablations may miss or underrepresent them. On the other hand, the presence of the micas and feldspars observed
during petrographic analysis probably accounts for the strong correlations for Al, Ba,
Ca, K, Na, and Rb; and the presence of biotite may explain the strong correlation of
Mg. These will all be more abundant than zircon and more likely to be accessed by
the laser during LA-ICP-MS.
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Table VII. Table showing Pearson’s correlation coefficients obtained when comparing all elements detected by both AD-ICP-MS and LA-ICP-MS across all geologic samples.
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Element
Pearson’s Correlation
Coefficent
La
Ce
Pr
Nd
Sm
Eu
Gd
Tb
Dy
Ho
Er
Tm
Yb
Lu
Al
As
Ba
Ca
Ce
Co
Cr
Cs
Cu
Fe
Hf
K
Li
Mg
Mn
Na
Nb
Ni
Pb
Rb
Sb
Sc
Sn
Sr
Ta
Ti
U
V
Y
Zn
Zr
0.27
0.17
0.24
0.29
0.25
0.17
0.04
0.42
0.49
0.44
0.41
0.42
0.20
0.32
0.94
0.60
0.92
0.37
0.18
0.03
0.97
0.76
0.13
0.71
0.15
0.96
0.97
1.00
0.96
0.99
0.99
0.21
0.43
0.97
0.86
0.11
0.32
0.82
0.81
0.93
0.31
0.59
0.04
0.69
0.13
Correlation
Coefficient (Outliers Removed)
0.67
0.75
0.77
0.62
0.65
Complete raw data sets for both forms of ICP-MS analysis are accessible at the
online databank, https://www.paleostrat.org/Public/projects.aspx?pide2bf1281b96a-48ad-85e3-9b2a8df29b6a.
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The negative correlation of Zr shown in Table VII may require a more complex
explanation. It is likely that incomplete digestion of Zr during sample preparation for
AD-ICP-MS (zircon is extremely refractory) caused AD-ICP-MS to under-represent
the presence of this trace element in our quartzites. LA-ICP-MS, however, probably
over-represented Zr because the highest standard (SRM612) used to calibrate most
elements only has about 40 ppm, a poor basis for calibrating data in the percent
range. The really crucial messages to draw from our comparison of AD- and LA-ICPMS results are that there are straightforward explanations for many of the positive
and negative correlations shown in Table VII; that refinement of both approaches (e.g.,
using more appropriate standards) will yield better results for both techniques; and
that the problem of the laser contacting too few spots and missing elements may be
resolvable by increasing the number of ablations per sample.
This said, and even as they currently stand, the pilot LA-ICP-MS data still show significant potential for discriminating among quartzite samples. We evaluated our
small LA-ICP-MS data set by creating bivariate plots of pairs of elements to ascertain which, if any, yielded meaningful sample groupings (e.g., of, most basically, replicate samples, and of samples deriving from the same geologic sources). In Figures
7, 8, and 9a, we present three of those bivariate plots: Sr–Fe, Ca–Fe, and Rb–Mn.
Regardless of geochemical technique, certain elements (or suites of elements, as
more sophisticated principal components analysis will eventually show with a larger
sample population) will be important for discriminating among samples and sources
and some elements will not. The examples we show here fall in the former category and
are intended to serve as exemplars of the potential of LA-ICP-MS to yield data that
can discriminate among quartzite sources.
The bivariate plot of Sr and Fe (Figure 7), for instance, properly groups the two
replicate samples 3-6 and 3-6a, replicate samples 5-1, 5-1a, and 5-1a-2 (a third replicate
of sample 5-1 was analyzed via LA-ICP-MS) and suggests compositional similarities
among the four samples from Source 4, a gravel deposit quarried prehistorically.
Plotting Ca and Fe (Figure 8) reinforces these findings, while also revealing that
samples 4-14 and 4-19 are diverging toward high Ca—something the Sr–Fe plot did
not and could not show. Finally, the Rb–Mn plot (Figure 9a) shows the same close
association of the 3-6 and 3-6a replicates; the 5-1, 5-1a, and 5-1a-2 replicates; and
clustering of the Source 4 samples—but this time on the basis of an entirely different pair of elements than shown in Figures 7 and 8. Rubidium, we should note, is a
mobile element, but this does not necessarily mean it is a poor choice for sourcing
analysis. It could be, for example, that mobility during formation determined local
differences in Rb concentration that are critical for outcrop discrimination. Future
analyses must evaluate such issues. We also note that all three bivariate
plots show samples 2-1 and 1-15—revealed through petrography to differ from other
samples—at some distance from samples discussed above, suggesting a convergence of LA-ICP-MS and petrographic data.
To assess how different the bivariate plots would look for our ostensibly more accurate AD-ICP-MS data versus our LA-ICP-MS data, we plotted the AD-ICP-MS results
for Rb and Mn (Figure 9b). Figure 9b shows that while there is predictably less
spread, for example, between the 3-6 and 5-1 replicate sets in the AD-ICP-MS data,
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Figure 7. Bivariate plot of strontium and iron in geologic samples as measured by LA-ICP-MS. Numbers
to the left of hyphens indicate source locality (1, 2, 3, 4, 5, or UMG). Samples with “a” are replicates
(duplicates of the same sample) and are enclosed in ovals.
Figure 8. Bivariate plot of calcium and iron in geologic samples as measured by LA-ICP-MS. Numbers
to the left of hyphens indicate source locality (1, 2, 3, 4, 5, or UMG). Samples with “a” are replicates
(duplicates of the same sample) and are enclosed in ovals.
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the structure of our AD- and LA-ICP-MS data is parallel. The Source 4 specimens
are somewhat homogenous and distinct from other sources in both plots; samples
2-1, 1-15, UMG-2, and 1-7 appear in roughly the same position in both plots; and even
UMG-1 and 3-8 plot relatively similarly regardless of how they were measured. These
complementary results are anticipated by the high correlation coefficient obtained
for Mn (0. 96) and Rb (0.97) shown in Table VII. They also drive home the crucial point
that LA-ICP-MS must be viewed at this stage of our nascent research as harboring
good potential for yielding results that will ultimately discriminate among Gunnison
Basin quartzites as well as, or nearly as well as, AD-ICP-MS—and without the troublesome requirement that they be crushed to a powder in the process. Future testing must thoroughly evaluate this hypothesis.
DISCUSSION AND CONCLUSIONS
First and foremost, our pilot efforts to develop methods to effectively discriminate
among different quartzite sources on a landscape (and eventually at Paleoindian
archaeological sites) suggest that the undertaking is anything but futile. We ourselves were, to a person, surprised at how “dirty” our quartzites are relative to the
cherts and flints that many researchers have previously attempted to geochemically
profile. Although some of our quartzites contained up to 98% SiO2 (like most microcrystallines), many had high levels of elements including iron, manganese, and a
host of others. In other words, although the perception by archaeologists and geologists alike has traditionally been that quartzites are ill-suited to fingerprinting and
sourcing, our modest results suggest the opposite: that at least Gunnison Basin
quartzites are anything but geochemically monotonous and that there is excellent
potential—given a lot more leg- and lab-work—to successfully discriminate among
sources and to eventually match cultural occurrences of quartzites to geologic sources
of that rock type.
In terms of which methods may work best in future efforts to conduct expanded
quartzite profiling, we conclude that petrography, a low-tech, low-cost analytical
strategy, will serve as an important component of a comprehensive quartzite profiling research strategy. Even low-power examination revealed significant variability
in our quartzite samples, even identifying one sample we believed at collection to
be quartzite as instead an altered volcanic tuff. As future research characterizes
more samples en route to developing a database of signatures of quartzite sources
in the Gunnison Basin and elsewhere, petrography may alone yield data that discriminate among sources. However, petrography’s greater value will probably lie in
its ability to help geochemists understand their trace-element data sets (helping
answer why, for example, a particular trace element appears anomalously high or low
in a sample from a particular geologic setting, or why two geochemically similar
samples must have derived from different sources).
Our geochemical results suggest that data obtained via AD-ICP-MS may have the
greatest potential of all the techniques we tested to discriminate among sources,
because crushing samples ensures that all trace elements present will be represented
in the profile. Indeed, AD-ICP-MS detected by far the greatest number of elements
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Figure 9. Bivariate plot of rubidium and manganese as measured by (a) LA-ICP-MS and (b) AD-ICP-MS.
Numbers to the left of hyphens indicate source locality (1, 2, 3, 4, 5, or UMG). Samples with “a” are replicates (duplicates of the same sample) and are enclosed in ovals.
of the geochemical analyses we performed. However, the crushing that produces
the most accurate results destroys samples. LA-ICP-MS impacts only the tiniest bit
of sample, virtually invisibly. Archaeologically, this is crucial, because even a rare
quartzite artifact could be profiled without compromising the integrity of the object.
However, it also means that trace elements will be missed if the laser does not
contact the mineral(s) containing them during ablation. That said, Table VII and
Figures 9a and 9b show that even with just four ablations, our LA data yield a discriminatory signal similar to that of AD-ICP-MS, and that LA-ICP-MS may therefore
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yield data sufficiently resolved that crushing samples for AD-ICP-MS will not be necessary.
The final and most obvious conclusion we draw from our pilot sourcing study of
twenty quartzite samples from six geologic sources and the Chance Gulch archaeological site is that while the potential for profiling discrete sources of quartzite
appears to be high—higher even than for microcrystallines that have received far
more attention from archaeologists and geologists alike—there is much work to be
done to determine (1) to what degree Gunnison Basin quartzites can be differentiated from one another, and (2) whether the apparently robust results in that region
can be replicated in other regions where quartzite sourcing could provide valuable
data. We consider quartzite sourcing now, in 2008, to be where obsidian and microcrystalline quartz sourcing were in the 1960s and 1970s, when researchers began
subjecting what would become thousands of samples to analytic techniques capable of fingerprinting them. We have the advantage, however, of knowing just how
many research doors opened as a result of that groundbreaking work, and we are
therefore optimistic that future efforts to profile quartzites in our study area and
around the world may pay similar dividends.
We conclude our report of our pilot-study experiments with comments on the
research directions that we see as the most important next steps in developing
a quartzite sourcing methodology for the Gunnison Basin (and anywhere else that a
researcher cares to explore quartzite sourcing). First and foremost, we must perform experiments to quantify how closely geochemical profiles generated by LAICP-MS profiles can approximate inherently more accurate AD-ICP-MS profiles. To
do this, our team plans to ablate representative quartzite samples repeatedly until the
mean values of the REE, U, Th, and other diagnostic elements stabilize. This will
then allow us to determine the amount of effort required to produce the highestquality data sets and whether this effort is tractable in sourcing artifacts.
We will then compare our results to AD-ICP-MS data generated for the same samples to look for systematic differences that can be attributed to matrix effects, calibration of the LA-ICP-MS laser, selection of standards, and so on (and further refine
our methodology accordingly). We performed four ablations per sample in our pilot
study and even that small number, when averaged, yielded results broadly comparable to AD-ICP-MS for many elements. Ten or more ablations per sample should
significantly increase the comparability of the two techniques. We hypothesize that
in the end, LA-ICP-MS will perform sufficiently well that we can proceed with largescale analysis of many samples using the procedure.
The next phase of research will require our team to sample as exhaustively as
possible quartzite sources in the Gunnison Basin. We must then perform both petrographic and LA-ICP-MS analyses of hundreds, even thousands of samples to ascertain the extent and nature of variability (1) within single cobbles from gravel sources
and within quartzite outcrops; (2) among gravels in a single, discrete gravel deposit;
(3) among Gunnison Basin gravel deposits and among exposed outcrops; and (4)
between gravel deposits and likely parent formations upstream.
In the end, if petrography and geochemical profiling do not reveal greater variability
between discrete sources of quartzite (whether gravel deposits or outcrops) than
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within them, we will never gain the ability to match quartzite Paleoindian artifacts
to sources on the landscape. However, we have shown that even our modest experiments suggest that some level of discrimination will be possible. Success will facilitate reconstructions of Gunnison Basin Paleoindian mobility strategies and pave
the way for other archaeological and geological studies that require effective methods for profiling quartzite.
The research reported here was funded through NSF grants SBE-0244922 and SBE-0604712. Neutron irradiations were provided by the OSU Radiation Center under the D.O.E. Reactor User Sharing Program. We
are grateful to Dennis Eggett, BYU statistician, for his help interpreting the AD-ICP-MS data and also to
John Dudgeon of California State–Long Beach’s IIRMES for his assistance conducting the LA-ICP-MS
analysis. We also are indebted to graduate students Caroline Myer and C.W. Merriman, who helped coordinate the quartzite collection and analytical efforts, as well as to the undergraduate field school students who helped us collect samples in summer 2005. We thank Scott Hughes (Department of Geosciences,
Idaho State University) for his help facilitating our INAA analysis and Craig Skinner (Northwest Research
Obsidian Studies Lab, Corvallis, Oregon) for graciously experimenting with XRF and our quartzite samples. Finally, we are grateful to Rónadh Cox, Drew Coleman, and an anonymous reviewer for suggestions that helped us significantly improve this manuscript.
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Author Queries
AQ1: Andrews 2005 not in Refs. Please supply.
AQ2: Krynine 1968 not in Refs. Should this be 1948? If not, please supply 1968 citation.
AQ3: Hughes 1994 not in Refs. Should this be 1992? If not, please supply new ref.
AQ4: Spielbauer 1984 not in Refs. Should this be 2005? If not, please supply new ref.
AQ5: Please specify publisher of DeWitt maps; also Gaskill et al. maps, Hedland map,
Odell maps, and Zech map at end of Refs.
AQ6: City of publication?