International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 Int
spatial inter-relationship between geospatial data in weighting
of spatial phenomena is essential.
Factor maps can be combined by using of conventioal models in
appropiate inference network. After section outlines the
conventional models with can be used for Mineral potential
mapping.
3. DESCRIPTION OF CONVENTIOAL MODELS
Different models exist for mapping mineral potential. These
models are based on data-driven and knowledge-driven. In this
section, conventional models for integrating data in mineral
deposit exploration are investigated.
Bolean modelling involves the logical combination of binary
maps resulting from the application of conditional AND and OR
operators. In practice, it is usually unsuitable to give equal
importance to each of the criteria being combined. Evidence
needs to be weighted depending on its relative significance.
Expert knowledge can not interfere in this model.
In Weight of Evidence models mineralization recognition
creteria by using the known mineral occurance (control points)
and statistical methods (Baysian theory), were wieghted and
integrated. This method only application in regions where the
response variable (e.g. distribution of known mineral
occurrences in the case) is fairly well known. This method is not
always applicable in mineral deposit exploration in detailed
stage but this model in the small scale is approprite method.
In Index overlay method, each class of every map is given a
different score, allowing for a more flexible weighting system
and the table of scores and the map weights can be adsjusted to
reflect the judgment of an expert in the domain of the
application under consideration.At any location, the output
score, S ,is defined as (equation 1)
SW. 4,
SW,
Where wi is the weight of the i-th map, and Ai is i-th map. The
greatest disadvantage of this method probably lies in its linear
additive nature.
(1)
In the Fuzzy Logic method, total of sheet maps (fuzzy
membership) based on the significance distance of features are
weighted (for each pixel or spatial position particular weight
between 0 to 1 is appionted). Five operators that were found to
be useful for combining exploration datasets, are the fuzzy
AND, fuzzy OR, fuzzy algebric product, fuzzy algebric sum and
fuzzy gamma operator (Bonham earter, 1994). These operatore
are briefly reviewed here.
The fuzzy AND operation is equivalent to a Boolean AND
operation on classical set. It is defined as (equation 2)
We
‘ombination 7 MINOF. , W > We tt) e)
Where W4 , Wg ,... is the fuzzy membership values for maps A,
B, ... at a particular location. This operation is appropriate
where two or more pieces of evidence for a hypothesis must be
present together for the hypothesis to be accepted.
The fuzzy OR is like the Boolean OR operation.This operator is
defined as (equation 3)
Ww.
Combination
= MAX(W WW +) 6)
A B C
This operatore where favorable evidences for the occurrence of
mineralization are rare and the presence of any evidence may be
sufficient to suggest favourability.
The fuzzy algebric product is defined as (equation 4)
n
W =I Ww, 4)
Combination
Where Wi is the fuzzy membership values for the i-th (i=
1,2...,n) maps that are to be combined. The combination fuzzy
membership values is alwayes smaller than ,or equal to, the
smallest contibuting fuzzy membership value, and is thus ,
‘decreasive’.
The fuzzy algebric sum operator is complementary to the fuzzy
algebric product, and is defined as (equation 5).
n
=1-( 1 (1-W,) J
= 1
l =
We
’ombination
The result of this operation is alwayes larger than , or equal to,
the largest contributing fuzzy membership value. The effect is
thus ‘increasive’. Two or more pieces of evidence that both
favour a hypothesis reinforce one another and the combined
evidence is more supporitve than either piece of evidence taken
individually
The fuzzy gamma operation is defined in term of the fuzzy
algabric product and the fuzzy algabric sum by (equation 6) Tabl
= (Fuzzy Agabric Sum)’ [5
He 'ombinaticn
H
* (Fuzzy Algabric Produet)" 6) [E
/ is a parameter chosen in the range (0,1), (Zimmermann and
Zysno, 1980). Judicious choice of gamma produces output (
values that ensure a flexible compromise between the
‘increasive’ tendencies of the fuzzy algabric sum and the =
‘decreasive’ effects of the fuzzy algabric product.
Evidence map can be combined together in a series of steps, by mi
using an inference network. The inference network an important occur
means of simulating the logical thought processes of an expert. Bu
Concerning the rule of conceptual modeling, the expert Buf
knowledge, existing data and characters of the models for Buf
combining factor maps, Index Overlay and Fuzzy Logic models T=
were selected in mineral deposit exploration in the detialed dyk
stage. Also integrated of Boolean operation, Index Overlay and
Fuzzy Logic models is checked and result of this model is | dv
investigated. Bufi
Bufi
4. CASE STUDY Gi
Buf
The arca of Rigan Bam is located at the 80 km south of the andes;
Rigan and 175 km southwest of the Bam city in Iran. This area roch
is a small part of a volcano-plutonic rocks operating in NW-ES [e
direction. The locatin of the area as well as ils geological map is rock t
illustrated in figure 1. Based on study discaussed on section 3, Buff
the mineralization recognition creteria(factors) of porphyry Buff
copper mineral deposit of Rigan Bam is appointed. i
ulm
With processing of input data, which was discaussed on section | m
3, factor maps are prepared. Figure 2 and Tabel 1 shown the meta
factor maps and the weighting for porphyry copper mineral furem
deposite of Rigan Bam respectively Me
i
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