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Journal of Geochemical Exploration 105 (2010) 95–105
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Journal of Geochemical Exploration
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j g e o ex p
Delineation and explanation of geochemical anomalies using fractal models in the
Heqing area, Yunnan Province, China
Jun Deng ⁎, Qingfei Wang, Liqiang Yang, Yanru Wang, Qingjie Gong, Huan Liu
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China
Key Laboratory of Lithosphere Tectonics and Lithoprobing Technology of Ministry of Education, China University of Geosciences, Beijing 100083, China
a r t i c l e
i n f o
Article history:
Received 30 August 2009
Accepted 9 April 2010
Available online 18 April 2010
Keywords:
Fractal
Geochemical anomaly
Sanjiang
Beiya
Yunan
a b s t r a c t
The Heqing area, located in the Sanjiang ore belt, China, consists of the Beiya gold orefield related to the
alkaline porphyry, the Emeishan volcanic mafic rocks and a series of sedimentary rocks. Thirty-nine elements
of stream sediment samples taken in the 1:200,000 geochemical survey in the Heqing area can be classified
into four groups using principal component analysis. Two fractal models, i.e., the concentration–area model
and the number–size model, are applied in determination of the thresholds for the representative elements
in the four groups. The thresholds obtained from the two models are similar. According to the thresholds, the
element concentration distribution can be divided into 3 segments, each of them is mainly correlated to one
type of rocks, including the alkaline porphyry related to gold-mineralized rocks, mafic rocks and sedimentary
rocks. This paper reveals that the various geological events can be characterized by the different fractal
models of element distribution.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
The explanation and delineation of anomalies from geochemical
backgrounds have a profound influence on the analysis of geological
evolution and the ore-formation process. Usually, the key to
geochemical data processing is to determine the threshold to separate
anomalies from the geochemical background and then either delineate
the mineralized areas or distinguish the anthropogenic and natural
sources of materials (Gałuszka, 2007; Hawkes and Webb, 1962).
Traditional methods for threshold determination involve iterative
mean ± 2σ statistical methods (Gałuszka, 2007; Hawkes and Webb,
1962), the box-plot method (Tukey, 1997), the fence method
(Schwertman and Silva, 2007; Schwertman et al., 2004), and other
techniques including probability graphs, univariate analysis, and
multivariate analysis (Stanley and Sinclair, 1989).
The distribution of element concentrations is inhomogeneous and
irregular; thus, fractal models are used to determine thresholds of
geochemical anomalies in recent decades. Fractal models are widely
applied in dealing with the element concentrations, including the box
counting model (Cheng, 1995; Deng et al., 2001, 2006; Mandelbrot,
1983), number–size model (N–S model) (Agterberg, 1995; Mandelbrot, 1983; Turcotte, 2002; Wang et al., 2010), radial–density model
⁎ Corresponding author. State Key Laboratory of Geological Processes and Mineral
Resources, China University of Geosciences, Beijing No. 29, Xueyuan Road, Beijing
100083, China. Tel.: + 86 10 82322301; fax: + 86 10 82321006.
E-mail address: [email protected] (J. Deng).
0375-6742/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.gexplo.2010.04.005
(Blenkinsop, 1994; Carlson, 1991; Carranza, 2009; Feder, 1988;
Mandelbrot, 1983; Raines, 2008), grade–tonnage model (Turcotte,
1997, 2002), self-affine model (Wang et al., 2007a), concentration–
area model (C–A model), distance–concentration model (Li et al.,
2003), multifractal model (Agterberg et al., 1996; Cheng, 1999; Deng
et al., 2007, 2008; Wang et al., 2008) and local singularity model
(Cheng, 2007).
The N–S model proposed by Mandelbrot (1983), the C–A model
proposed by Cheng et al. (1994) and local singularity model developed
by Cheng (2007) are widely applied to separate anomalies from
backgrounds in metallic geochemical exploration (Agterberg, 1995;
Carlson, 1991; Turcotte, 2002; Wang et al., 2007a), oil/gas prospecting
(Zhang et al., 2006) and environmental studies (Lima et al., 2003), and
they show more effectiveness than the traditional methods, such as
the iterative mean ± 2σ method. The local singularity model is used for
find out the weak anomalies in a low background; and the N–S model
and C–A model have advantage in analyzing the overall structure of the
geochemical data and map. Although the N–S model and C–A model
are widely applied, their comparison is rarely carried out.
The application of the fractal models also suggests that the different
mineralization intensity can be distinguished by the fractal distribution of the geological objects (Mandelbrot, 1983; Turcotte, 1997;
Roberts et al., 1998; Deng et al., 2009). For instance, with the increase
of mineralized rank, the dimensions of the fractal models of the grade,
vein thickness, and regional structure density tend to be smaller (Deng
et al., 2001; Monecke et al., 2001; Roberts et al., 1998). The smaller
dimension denotes a greater proportion of the objects with greater
intensity or density. In one region, especially the ore cluster area,
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J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
various geological events, including the sedimentation, volcanism and
metallogenesis, occurred and formed a complex and combined spatial
distribution of various geological objects. The abundances of various
elements in the geological objects are totally distinct. Is it still possible
to utilize the fractal models of element distributions to detect the
different geological objects?
The Heqing area, located in the Sanjiang ore belt, China, comprises
the Beiya gold poly-metal orefield related to alkaline porphyry and
the mafic basalts, which belong to the Emeishan large igneous
province (LIP). Taking the Heqing area as an example, this paper
utilized the N–S and C–A fractal models to describe the distribution of
regional geochemical data, to compare the results obtained via the
two different models and to separate the various geological objects.
The alkaline porphyry and Emeishan LIP developed within a large area
of southwestern China are genetically related to numerous Cu, Au,
PGE, Pb and Zn deposits. Thus, the results of this study can provide
references for understanding the geological and geochemical background of ore-formation processes in this and other areas with similar
geological settings.
2. Geological settings
The Heqing area is located at the east of the Jinshajiang suture, at
the boundaries of the western part of the Yangtze Plate. Rocks in the
area comprise Late Permian basalt, Lower Triassic sandstone, Middle
Triassic limestone, Jurassic shale and sandstone, Cretaceous sandstone
and Tertiary lacustrine and Quaternary sedimentary rocks (Fig. 1b).
The outcrops in the Heqing area can be divided into three parts from
west to east according to the lithology. The outcrops in the west are
mainly Triassic, those in the middle part are Permian volcanic mafic
rocks, and those in the east include the Jurassic and Cretaceous,
Triassic systems, tertiary alkaline porphyry plugs and several related
metallic ore deposits (Fig. 1).
The mafic rock in this area is a portion of the Emeishan LIP. The
Emeishan LIP is generated by mantle plume, covering an area of more
than 2.5 × 105 km2 across the Yunnan, Guizhou, Sichuan and Guangxi
provinces, with a total thickness ranging from several hundred meters
up to 5 km (Xu et al., 2001; Deng et al., 2010). Many reliable
radiometric age dates (zircon U–Pb SHRIMP) indicate that emplacement occurs at ∼ 260 Ma (Ali et al., 2005; Zhou et al., 2002). In the
central part of the Emeishan LIP, several intrusions contain substantial
V–Ti-magnetite deposits and Ni–Cu–platinum group element (PGE)
sulfide deposits (Song et al., 2003; Zhong et al., 2002).
Tertiary alkaline porphyry plugs related to the Au, Ag, Pb and Zn
mineralization are abundant in the Heqing area. The famous Beiya
gold poly-metal orefield is located in the southwestern part. Tertiary
alkaline porphyry plugs in the Beiya area include quartz–albite
porphyry, quartz–K–feldspar porphyry and biotite–K–feldspar porphyry. Both the quartz–albite porphyry and quartz–K–feldspar
porphyry are associated with porphyry Cu–Au and skarn-type
polymetallic deposits (Xu et al., 2007). The age of the quartz–albite
porphyry at Wangdongshan is 65.56 ± 0.28 Ma (Xu et al., 2006). The
three quartz–K–feldspar porphyritic bodies, at Wangdongshan,
Hongnitang and Weiganpo, have intrusive ages of 32.50 ± 0.09 Ma,
25.89 ± 0.13 Ma and 25.53 ± 0.15 Ma, respectively (Xu et al., 2006). A
number of Tertiary alkaline porphyry bodies also occur along the
Jinshajiang suture, some of which are associated with important
economic mineral deposits, including porphyry Cu deposits in the
Yulong area, porphyry Cu and Mo deposits in the Machangqing area
and the Yaoan area. The alkaline porphyries in the Beiya area and
alkaline porphyries in western Yunnan in general resulted from
partial melting of large masses of rocks along the major Jinshajiang
shear zone at the contact between the buried Palaeo-Tethyan oceanic
lithosphere and upper mantle lithosphere (Xu et al., 2007).
3. Geochemical data and principal component analysis
The 3rd Geological Team of the Yunnan Bureau of Geology and
Mineral Resources has carried out a regional geochemical survey at a
scale of 1:200,000 in the Heqing region, as part of the RGNR project
Fig. 1. Geological map of the Heqing area, Yunnan Province, China. Q–E represent Quaternary to Tertiary, J is Jurassic, K is Cretaceous, P is Permian, Pe is Permian mafic rock, C is
Carboniferous, D is Devonian, S is Silurian, O is Ordovician.
J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
Fig. 2. Geochemical maps of the selected elements: (a) Au (10− 9), (b) Pb (10− 6), (c) Cu (10− 6), (d) Co (10− 6), (e) Y (10− 6), (f) Zr (10− 6), (g) U (10− 6), (h) Li (10− 6).
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J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
(Xie et al., 1997; Wang et al., 2007b). A total of 1 672 stream sediment
samples were collected, and 39 elements, such as Au, Ag, Zn, Cu, Co,
Ni, and U, were analyzed (Wang et al., 2007b). These element
concentration values are used in this study.
When a large geochemical data set is generated, it may be helpful to
use a principal component analysis (PCA) in order to reduce the
number of observed variables to a smaller number of factors, providing
insight into the structure of the variance of the parameters (Muller
et al., 2008). In PCA, the concentration dataset is divided into several
subsets, represented by different factors. The elements in the subset
are correlated with one another and largely independent of the
elements in the other subsets. Here, factors should to be representative
of the underlying geological and metallogenical process that created
the correlations among these variables. In this paper, a principal
component analysis was carried out using SPSS statistical software
package.
In PCA, it revealed that the 39 elements could be typically classified
into the following four groups, with each group controlled by different
factors: (1) Au, Pb, Zn, and Ag, (2) Cu, Co, Mn, P, V, and Fe, (3) Be, Nb,
Zr, Th, and Y and (4) Li, F, and U. The first group, including Au, Pb and
Zn, is shown to be concentrated in the Beiya gold orefield in the
southwestern part of Heqing area. Elements of the second group,
including Co, Ni, and Cu, are enriched in the area with Emeishan mafic
rock. The elements in the third group are concentrated in a wider area
than those in the first and second groups. The elements in the fourth
group are enriched in the area with outcrops of sedimentary rocks
(Fig. 2; Table 1).
4. Fractal models
The N–S model is utilized to describe the distribution of geochemical disregarding spatial distribution. The model is expressed by
the following equation:
Nð≥rÞ = cr
−D
ð1Þ
where r denotes the element concentration in this paper; N(≥r) is the
cumulative number of concentration greater than or equal to r; c is a
constant, and D is the scaling exponent or fractal dimension of the
concentration distribution. Plotting the cumulative number against
concentration in ln–ln coordinates, i.e., ln N( ≥ r) versus lnr, results
in a straight line with the slope − D within a concentration interval.
Other common distributions such as normal (Gaussian), log-normal
or exponential yield distinctly curved graphs with the ln N( ≥ r) versus
ln r plot.
In the C–A model applied to analyze the structure of geochemical
maps, in the map the area A(r ≥ ri) enclosed by contours with
concentration ri has a power–law relation with ri as follows:
−D
Aðr ≥ri Þ = cri
ð2Þ
Table 1
Principal component analysis of the elements in the stream sediment samples from the Heqing area, Yunnan Province, China.
Element
Cu
Pb
Co
Ni
Li
F
U
W
Mo
Hg
As
Sb
Bi
Cd
Be
B
Sn
Ag
Au
Cr
Nb
Sr
Mn
Ti
Zr
Th
P
V
Y
Ba
La
Zn
Si
Fe
Al
Ca
Mg
K
Na
Component
1
2
3
4
5
6
7
8
0.883
0.028
0.891
0.353
− 0.314
− 0.027
− 0.028
0.057
0.258
− 0.020
0.006
0.053
− 0.028
− 0.008
0.102
− 0.551
0.269
0.025
0.123
0.244
0.614
0.540
0.800
0.944
0.369
0.112
0.813
0.941
0.491
0.155
0.086
0.222
− 0.752
0.939
0.371
0.141
0.105
− 0.226
0.004
0.005
0.036
0.169
0.059
0.135
0.172
0.333
0.088
0.146
0.093
− 0.036
0.014
− 0.010
0.118
0.740
0.077
0.590
− 0.007
0.063
0.072
0.712
− 0.004
0.238
0.173
0.758
0.731
0.175
0.151
0.751
0.173
0.748
0.084
− 0.289
0.222
0.492
− 0.149
− 0.178
0.520
0.016
0.082
0.948
0.020
0.053
0.072
0.184
0.072
0.230
0.060
− 0.035
0.205
0.587
0.592
0.587
0.146
0.041
0.156
0.904
0.743
0.032
− 0.012
− 0.012
0.357
− 0.016
0.005
0.054
− 0.031
0.034
0.103
0.008
− 0.022
0.784
− 0.093
0.060
0.107
0.025
0.032
0.056
− 0.011
− 0.100
0.064
0.063
0.166
0.786
0.812
0.797
0.157
0.664
− 0.063
0.224
0.253
0.079
− 0.013
0.340
0.448
0.122
0.088
0.024
0.054
− 0.082
− 0.091
0.027
− 0.081
− 0.162
0.431
0.003
0.099
0.296
− 0.026
0.295
0.085
− 0.287
0.024
0.564
0.003
0.018
0.375
− 0.081
0.030
− 0.016
0.326
0.870
0.184
0.073
0.097
0.056
− 0.109
0.033
− 0.030
0.030
− 0.030
0.108
0.019
0.052
0.166
− 0.023
0.033
0.882
− 0.040
− 0.059
0.064
0.075
− 0.187
0.094
0.023
0.164
0.087
− 0.083
0.042
0.012
− 0.210
0.141
0.271
0.015
0.410
− 0.042
− 0.069
0.011
0.003
− 0.011
0.056
− 0.237
0.102
− 0.055
− 0.045
0.033
0.012
0.093
− 0.037
0.035
− 0.014
− 0.246
− 0.133
0.050
0.001
0.012
0.069
0.053
0.233
− 0.006
0.038
− 0.061
− 0.119
0.111
− 0.014
0.003
0.071
− 0.165
− 0.023
− 0.315
− 0.013
− 0.171
0.897
0.787
0.142
0.274
0.060
0.003
0.053
− 0.010
− 0.070
0.208
− 0.113
0.064
− 0.206
− 0.012
− 0.048
− 0.027
− 0.034
0.050
0.242
0.088
− 0.045
− 0.001
− 0.003
− 0.133
− 0.121
0.674
0.072
0.002
− 0.172
0.243
0.261
0.005
0.034
0.898
0.140
0.025
− 0.063
0.043
0.148
− 0.013
0.019
0.413
0.135
0.035
0.047
0.001
0.048
0.058
0.119
0.116
0.733
0.033
0.605
0.367
0.584
0.071
− 0.017
0.065
0.076
0.021
0.079
− 0.004
0.045
0.022
0.007
0.041
− 0.010
0.013
0.077
0.035
0.013
0.078
− 0.018
0.086
0.389
− 0.070
0.012
0.075
0.043
0.034
0.048
− 0.018
The bold italicized numbers denote the thresholds used in this paper.
J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
where D still denote the fractal dimension for the C–A model. Similar
to the N–S model, the lnA–lnr plot results in a straight line with the
slope − D. ArcGIS was used to analyze the element concentration via
the C–A model.
5. Delineation of geochemical anomalies
Fig. 3 illustrates the histograms for log10Au and log10Cu (Fig. 3a, c)
and a “quantile–quantile” plot (Q–Q plot) (Fig. 3b, d). The Q–Q plot
shows that the concentrations don't follow a log-normal distribution.
Thus, the element concentrations can be described by the two fractal
models.
The concentration distributions of various elements are shown to
have bifractal characteristics. The plots in the ln–ln coordinates are
fitted by three line segments for most elements and by two line
segments for a few elements. The N–S and C–A fractal models of the
typical elements in various groups are illustrated in Fig. 4 and Fig. 5;
the thresholds and fractal dimensions are listed in Table 2. The Di
represents the fractal dimension in the ith segments and Ti is the
threshold between the (i−1)th and ith segments.
The N–S fractal model of gold comprises three line segments in ln–
ln coordinates (Fig. 4a). The concentrations in the first segment
are distributed in the Jurassic and Cretaceous strata, and the
corresponding fractal dimension is relatively small; those in the
second segment, with a much greater fractal dimension, are mainly
distributed in the Triassic in the eastern part, the Emeishan mafic
rocks in the central part and in the locations where the magmatic
intrusions develop in the southeastern part; the concentrations in the
third segment, with a small fractal dimension, correspond to the gold-
99
mineralized areas around the Beiya orefield (Fig. 6a, b). The fractal
dimension in the third segment is smaller than that in the second one,
showing that the greater concentrations dominate in the mineralized
area, corresponding to the contrast results of the fractal dimensions of
element concentration distributions in the non-mineralized area and
those in the mineralized area (Deng et al., 2009). The C–A fractal
model of gold also comprises three line segments, and the first
threshold in this model is consistent with that obtained from the N–S
model, yet the second threshold is much greater than that in the N–S
model, suggesting that the C–A model can denote a much smaller area
of mineralization (Fig. 6c). In the C–A model, the fractal dimensions
increase from the first segment to the third one, indicating that, with
increasing contour concentrations, the enclosed area decreases more
rapidly in the mineralized area than in the non-mineralized area
(Fig. 5a). The fractal dimensions obtained from the C–A model and N–
S model are different, and they can reveal the data distribution from
different perspectives.
Since Pb and Au are clustered the same group in the principal
component analysis, the N–S model and C–A model of Pb are similar
to those of Au, and the thresholds in the N–S model are
approximately equal to those in the C–A model (Figs. 4b and 5b);
in light of the thresholds, the element map can be separated into
three parts as shown in Fig. 6d, e, and f, which are analogous in spatial
distribution to the three corresponding parts for Au. The area with
the Pb anomaly is greater than that with the Au anomaly, indicating
the Pb is more mobile than the Au in the surface environment of the
study area.
The thresholds of Cu and Co in the N–S model generally correspond
to those in the C–A model (Fig. 4c, d; Fig. 5c, d). Based on the
Fig. 3. Q–Q plots and histograms for Au and Cu concentrations. (Data are base 10 log-transformed). (a) Q–Q plot for Au; (b) histogram for Au; (c) Q–Q plot for Cu ; (d) histogram for Cu.
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J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
Fig. 4. N–S model of the typical elements in the Heqing area, Yunnan Province, China: (a) Au, (b)Pb, (c) Cu, (d) Co, (e) Y, (f) Zr, (g) U, (h) Li.
thresholds of Cu and Co, the area where the sedimentary rocks
developed and that with the Emeishan mafic rocks can be generally
separated (Fig. 7), similar to the analysis of Au and Pb. The Cu and Co
anomalies are only located in parts of the Emeishan mafic rocks,
suggesting that the anomaly is probably induced by the hydrothermal
activities related to the volcanic rocks.
The thresholds of Y and Zr in the N–S model and those in the C–A
model are approximately equal. The distributions of Y and Zr can also
be separated via two steps according to the two thresholds (Fig. 4e, f;
Fig. 5e, f). The area with high Y and Zr concentrations overlaps the
locations with high Cu and Co concentrations, and the areas with high
Au and Pb concentrations, indicating the activation of both the
hydrothermals related to the alkaline porphyries and the hydrothermals related to the mafic rocks, can induce enrichment of the
elements in the third group, most of which are HFSEs (High field
strength elements) (Fig. 8).
Similarly, the area with Jurassic and Cretaceous rock and that with
Emeishan mafic rock in the Li and U geochemical maps can generally
be separated via the thresholds in the N–S and C–A models (Fig. 4g, h;
Fig. 5g, h; Fig. 9). The Li and U are mainly enriched in the Triassic area,
especially in the boundary between the Triassic and Permian mafic
rocks.
The concentrations of Cu, Co, Li and U in the rocks are much lower
than the standards of ores. In the N–S fractal models of Cu, Co, Li and U,
the fractal dimensions increase from the first segment to the third
one, indicating that the proportion of the greater concentrations
decreases, which is distinct from the mineralization elements as Au
and Pb.
6. Discussions and conclusions
The Heqing area, located in the Sanjiang ore belt, China, comprises
the Beiya gold poly-metal orefield related to alkaline porphyry,
Emeishan volcanic mafic rocks and sedimentary series. The explanation and delineation of geochemical anomalies in the region can
provide important information for understanding of ore systems of the
Heqing region or even the nearby ore cluster related to the alkaline
porphyry or Emeishan volcanic mafic rocks. The 39 elements obtained
J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
101
Fig. 5. C–A model of the typical elements in the Heqing area, Yunnan Province, China: (a) Au, (b)Pb, (c) Cu, (d) Co, (e) Y, (f) Zr, (g) U, (h) Li.
N–S model and the C–A model are different, suggesting the oreforming elements show more complex distribution.
According to the thresholds, the element concentrations can be
divided into 3 concentration segments or populations, each of which
from a stream sediment survey can be typically classified into four
groups. Two fractal models, the C–A model and the N–S model, are
applied in determination of the geochemical thresholds. For the oreforming elements of the first group, the thresholds obtained from the
Table 2
Thresholds and fractal dimensions of the N–S and C–A models of the typical elements in the Heqing area, Yunnan Province, China.
Element
Au
Pb
Cu
Co
Y
Zr
U
Li
T1
0.8
6.3
5.1
4.5
10.5
172
1.1
0.38
D1
T2
D2
T3
D3
rmax
N–S
C–A
N–S
C–A
N–S
C–A
N–S
C–A
N–S
C–A
0.76
0.32
0.09
0.2
0.59
0.51
0.07
0.12
0.41
0.08
0.02
0.11
0.28
0.31
0.23
0.14
2.3
17.2
24.4
11.9
22.1
272
1.6
31.8
2.03
17.2
19.5
13.7
22.06
271.99
1.9
33.1
2.86
2.00
0.77
1.93
3.21
2.6
2.57
2.62
1.78
3.98
0.93
1.11
2.91
3.23
2.83
4.42
6.1
62
173.3
49
33.2
526
7.6
57.1
24.4
45.8
140.6
55.7
31.36
525.98
7.6
57.1
1.05
0.95
5.01
10.95
12.84
5.79
4.39
4.33
3.98
0.92
5.1
18.14
16.92
11.97
7.85
13.44
rmin and rmax represent the element minimum and maximum concentrations respectively. The bold italicized numbers denote the thresholds used in this paper.
120
5686.5
1333.3
81.1
64.7
1260
185.7
16.19
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J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
Fig. 6. Anomaly delineation of Au and Pb. (a) Au anomaly delineation with the first threshold in the N–S model; (b) Au anomaly delineation with the second threshold in the N–S
model; (c) Au anomaly delineation with the second threshold in the C–A model; (d) Pb anomaly delineation with the first threshold in the N–S model; (e) Pb anomaly delineation
with the second threshold in the N–S model; (f) Pb anomaly delineation with the second threshold in the C–A model (The grey points are not involved in the calculations of fractal
models in Fig. 6d, e, g, h).
is generally distributed in the area dominated by a single type of
geological event. It shows that porphyry-related hydrothermal
activations tend to result in the enrichment of the Au–Pb–Zn–Ag
group, that hydrothermal activations in the Emeishan LIP correlated
with the enrichment of the Cu–Co–Mn–P–V–Fe group, that the
hydrothermals related to the porphyry or those related to the mafic
rock can both induce the concentration of HFSEs, and that the
sedimentary rocks are more likely to cause high concentrations of
the Li–F–U group than porphyry and mafic rocks. It further indicates
that the sedimentary series, volcanic rocks and mineralization
events are characterized by the different fractal patterns of element
distributions.
Acknowledgements
This research is supported by the National Basic Research Program
(No. 2009CB421000), the National Natural Science Foundation of
China (Grant nos. 40572063, 40672064 40872194, and 40872068),
the 111 project (No. B07011), and the Program for Changjiang
Scholars and Innovative Research Team in University (PCSIRT).
J. Deng et al. / Journal of Geochemical Exploration 105 (2010) 95–105
103
Fig. 7. Anomaly delineation of Cu and Co. (a) Cu anomaly delineation with the first threshold in the C–A model; (b) Cu anomaly delineation with the second threshold in the C–A
model; (c) Co anomaly delineation with the first threshold in the N–S model; (d) Co anomaly delineation with the second threshold in the N–S model.
Fig. 8. Anomaly delineation of Y and Zr. (a) Y anomaly delineation with the first threshold in the N–S model; (b) Y anomaly delineation with the second threshold in the N–S model;
(c) Zr anomaly delineation with the first threshold in the N–S model; (d) Zr anomaly delineation with the second threshold in the N–S model.
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