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Like several other chemical elements in the Gejiu area, arsenic in the surficial
deposits that were sampled shows two types of anomalies. The arsenic local singu-
larity map for arsenic shows many relatively small anomalies (where
2) across
these anomalies is spatially correlated with occurrences of (mined and unmined)
mineral deposits. The arsenic concentration values are highest in the eastern part of
the area. Together the highest values describe a large irregularly shaped anomaly that
is probably caused by mining activities restricted to this part of the area.
Figure
11.26g, h
will show frequencies of As local singularities and As concen-
tration values, respectively. Suppose that parameters describing the two preceding
anomaly types are identified by the subscripts 1 (for local singularity anomalies)
and 2 (for the high-concentration anomaly). From
ʱ<
3.0178 (estimated slope of
β
2
¼
¼
0.258. From
E
ʱ2
with
ʾ
¼
2 km in this application, it follows that estimated slope of
straight-line in Fig.
11.26g
(
c
·
E
¼
¼
2.6474) provides an estimate of
β
1
¼
8.7945.
Consequently,
d
1
(As)
is a parameter estimated by the slope
of the best-fitting straight-line on a C-A plot. Then this estimate can be converted
into either the fractal dimension
D
(
¼
0.082. Suppose that
β
¼
2/
β
) or into the index of dispersion
2
D
/2
d
(
1) characterizing the non-linear process. In terms of Fig.
10.21
:ifa
block with high-concentration value (
¼
) is divided into two halves, the concentra-
tion values of the halves are, on average, equal to (1 +
d
)
ʾ
,
respectively. Thus a higher index of dispersion means stronger spatial variability.
The small anomalies (where
ʾ
and (1
d
)
ʾ
0.082 have lower dispersion
index than the broad regional anomaly restricted to the eastern part of the area
with
d
2
(As)
ʱ<
2) with
d
1
(As)
¼
0.258. Several other elements (tin, copper, silver, gold, cadmium,
cobalt, iron, nickel lead, and zinc) show anomalies similar to those for arsenic
(Cheng and Agterberg
2009
). The first type (local singularities) is useful for
exploration because it provides indicators for buried ore-bodies. The second type
helps to describe regional pollution due to mining activities. The shapes of two
kinds of anomalies (1 and 2) are markedly different, and this probably is the main
reason that a clear distinction could be made between the two underlying enrich-
ment processes (proximity to buried mineral deposits and pollution due to mining
activities) in this example of application.
¼
References
Afzal P, Alghalandis YF, Khakzad A, Moarefvand P, Omran NR (2011) Delineation of mineral-
ization zones in porphyry Cu deposits by fractal concentration-volume modeling. J Geochem
Explor 108:220-232
Agterberg FP (1980) Mineral resource estimation and statistical exploration. In: Miall AD
(ed) Facts and principles of world oil occurrence, Canadian Society of Petroleum Geologists
Memoir 6. Canadian Society of Petroleum Geologists, Calgary, pp 301-318
Agterberg FP (1981) Geochemical crustal abundance models. Trans Soc Min Eng AIME
268:1823-1830
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