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locations were 2 km apart both in the north-south and east-west directions. It
shows a large anomaly in the eastern part of the Gejiu area surrounding most large
tin deposits including the mines. The three large tin deposits in the central part of
the Gejiu area are recent discoveries. To illustrate in detail how singularities were
estimated 12 different locations were arbitrarily selected on the map as shown on
Fig. 11.23b . In total, 1,056 local tin singularities were estimated (Fig. 11.23d )by
assembling the tin concentration values from within the same 26
26 km 2 cells
used for the moving average map (Fig. 11.23c ). For example, Fig. 11.24 illustrates
in detail how singularities were estimated at two of the 12 different locations. The
singularities were estimated by fitting straight lines on log-log plots of either the
concentration value ( x )versus
E α 2 ,oramountofmetal
2
using x
¼
c ·
ʼ ¼
x ·
E
E
E α .InFig. 11.23d , both methods are used yielding similar
estimates of the singularity
versus
using
ʼ ¼
c ·
E
.
The main difference between the patterns of Fig. 11.23c, d is that lower and
higher local tin singularity values are more evenly distributed across the Gejiu area
than the tin concentration values themselves. The preceding analysis was repeated
for arsenic, which is a toxic element. Its moving average and local singularity
patterns (Fig. 11.25 ) are similar to those obtained for tin (Fig. 11.23 ).
The histogram of all local tin singularities is unimodal (Fig. 11.26a ). Strength of
spatial correlation between a point pattern and a contour map can be estimated by
means of the weights of evidence method (Sect. 5.1.4 ) . The Student's t -value diagram
(Fig. 11.26b ) was used to express statistical significance of strength of spatial
correlation between (a) point pattern of the 11 tin deposits (Fig. 11.23a ), and
(b) the local tin singularity map (Fig. 11.23d ). Although there are relatively few tin
deposits, t -values near the peak (where t
α
2.0
representing 95 % confidence level for statistical significance indicating positive
correlation between the two patterns. In total, 93 local
¼
4.84 at
α ¼
1.925) exceed t 0.05 ¼
tin singularities have
α<
1.925. Their combined area measures only 8.8 % of total study area, but
61.3 % of the tin deposits occur within this relatively small low-singularity sub-area.
The logarithm (base e) of number of local tin singularities exceeding
is linearly
related to α (Fig. 11.23c ) with slope of approximately 2.3. Likewise, there is an
approximate straight-line relationship (slope¼ 3.2) between logarithmically
transformed cumulative area and largest tin concentration values (Fig. 11.23d ).
This is an example of a concentration-area (C-A) plot (Sect. 10.3.1 ). The pattern
on a C-A plot was automatically broken into three successive straight-line segments.
For tin (Fig. 11.26d ), log e α
α
of the third segment extends from 7.414 to 8.637 with
best-fitting straight-line: y
26.9230-3.1576 x . It represents a Pareto frequency dis-
tribution for the highest concentration values. Other chemical elements enriched in
the hydrothermal tin deposits show patterns similar to those obtained for tin
(Fig. 11.26a-d ) as illustrated for arsenic (Fig. 11.26e-h ). For arsenic, the second
straight-line segment in the C-A plot (Fig. 11.26h ;alsoseeFig. 10.30 ) extends from
7.147 to 7.955 with best-fitting straight-line: y
¼
¼
25.6069-3.0178 x as previously
discussed in Sect. 10.4.
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