anbul 2004 
Networks 
porphyry 
whvsie 
ophysic 
omaly 
lex ov. 
lex ov. 
|. Y708 
; , y=08 
3,y=08 
1 , y=08 
; , y=08 
; ,y=08 
; ,y=08 
3, 08 
3 ,y708 
3, y=08 
  
ential map 
verlay 
y=0.9 
Wr 
209 
yverlay 
  
  
   
pmes 
in depth 
um drillholle 
  
International Archives of the Photogrammetry, Remote Sensi 
Table 3: Results of comparing and evaluation of the 
result of drilling and the results of mineral potential map 
of Rigan Bam by applying approprite inference networks 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
[ Fuzzy logic method 
9. Drillhole Class of Weight 
NO Drillhole | DHM-4 Evaluate 
l Best 0.86 - 
2 Weak 0.41 4 
3 Medium 0.82 v 
4 Medium 0.70 + 
5 Weak 0.74 - 
6 Medium 0.86 + 
3j Weak 0.0 + 
8 Weak 0.64 + 
F- Sum 6 
Integrated method Integrated method 
di DASS evaluate | Weight DHM=10 evaluate 
] 0.78 - 0.90 + 
2 0.38 + 0.51 + 
3 0.72 + 0.84 + 
4 0.58 - 0.73 + 
5 0.68 + 0.81 - 
6 0.87 + 0.96 - 
7 0.0 + 0.0 T 
8 0.59 4 0.67 * 
D. 6 6 
  
  
As can be seen in table 3, three appropriate inference 
networks (LN) were selected (one of the Fuzzy Logic 
network and two of the integrated networks). Results of 
three selected networks are in a good accordance with 
drilling results (9675). Mineral potential maps of this area 
produced by the appropriate inference networks are 
shown in figures 5. 
1107 
ig and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
  
  
  
figure 5: Map Mineral Potential of porphyry copper 
mineral deposite of Rigan Bam 
A: Fuzzy Logic method (I.N= 4) 
B: Integrated method (I.N=6) 
C: Integrated method (LN-10)