tanbul 2004
3mentation -
-scale image
Angewandte
Wichmann,
Ii, F. A. M.
ion growing
ote Sensing
T, C. 1996.
schetti, G. et
rture Radar.
'mentierungs-
en mit SEQ,
> Software >
segmentation
e Processing,
ew Adaptive
ry. Technical
Science lll,
ssification of
issification of
1 Geoscience
otential use of
areas — First
ification and
al monitoring
urban areas.
6-205.
eementation
seg
7-1294.
& Golden, M,
‚es. Technical
date algorithm
p. 1461-1464
Foundation for
high resolution
1592/1-2). For
« Ms. Prietzsch
ber ( University
n).
MINERAL POTENTIAL MAPPING OF COPPER MINERALS WITH GIS
M. Karimi M.J. Valadan Zoej :
[,2: Faculty of Geodesy and Geomatics Eng., K.N.Toosi University of Technology, Tehran, Iran
1: mk_karimi@yahoo.com, 2: valadanzouj@kntu.ac.ir
Commission WG IV/1
KEY WORDS: GIS. Mapping, Geology, Modelling, Integration, Combination
ABSTRACT:
Lack of a systematic idea for collecting, managing and integrating various geo-spatial data (rom dif
scales make mineral deposit exploration to be encountered with dif
exploration activities are geo-spatial, GIS can describe and anal
ferent sources and in different
ficulties. Since most of the information related to mineral deposit
yse interactions, to make predictions with models, and to provide
support for decision-makers. Steps of mineral potential
preparation and structuring, producing factor maps
proposed. For experimental test, the mineral potential ma
other deposit.
I. INTRODUCTION
Mineral exploration is a multi-stage activity that begins at a
small scale and progresses to large scale. In each stage,
topographical, geological, geochemical, geophysical data
collected, processed and integrated. After analysing each stage
produced mineral potential map and the study area becomes
smaller.
Mineral potential mappig with using of conventional methods
are very difficult and sometimes impossible. Geographical
Information System (GIS) has potential for storing, updating,
retreiving, displaying, processing, analysing and integration of
different geo-spatial data. In order to overcome difficulties such
as: large mass of data, existence of data in the analogue form,
non-existence of stanards and related directions for collecting,
managing and processing the data, differnet environments for
Storing and processing, non-existence of an environment for
Integrating data into conventional models in mineral deposit
exploration, using of GIS is essential.
In this paper, after an introduction, Steps of Mineral potential
mapping is outlined in section 2. section 3 outlines the
conventional models with can be used for Mineral potential
mapping. Evaluation of appropriate models in Rigan Bam
Copper deposit are presented respectively in the section 4. And
finally the paper is concluded in section 5.
2. MINERAL POTENTIAL MAPPING
In Mineral deposit exploration the divers maps, each having
Med specifications, are collected, processed and integrated.
fler analysing each stage, mineral potential map is produced.
mapping includes identify mineralization recognition criteria, data
and combining of factor maps in the appropriate inference networks.
In this research, conventional models for combining factor maps have been investigated and index overlay
were selected in mineral deposit exploration in detailed stage. Also an integr
and fuzzy logic models
ation model using of appropriate models have been
p of Rigan Bam copper deposit in the south east of Iran, with appropriate
methods in different inference networks have been produced and 3 appropriate inference networks (
model and two networks by integrated model) are selected. Results of three-selected network
results (7575). Proposed model in Rigan Bam deposit capability with required
one network by Fuzzy Logic
are in a good accordance with drilling
variation can be used for mineral potential mapping in
The most important aspect of mineral deposit exploration is the
mineral potential mapping composing of following steps:
* Identifying mineralization recognition criteria
° Data preparation and structuring
e Producing factor maps
e Combining of factor maps in the appropriate inference
networks.
Mineralization recognition criteria is identified based on
mineral deposit model (conceptual model) and expert
knowledge. In conceptual modeling of copper deposite
exploration, total mineralization recognition criteria is
appointment and relation between factors (criteria) are defined
and presented in an ERD (Entity Relationship Diagram). Then
all the appropriate data gatherd into a GIS environment. In GIS
the input layers are processed, based on the following
functionalities, and the factor map is extracted.
e — Mapreclassification
* Producing Proximity Map
e Operation on attribute tables
* Spatial, topological and geometrical modeling
° Producing Geochemical and geophysical anomaly map
e Assigning appropriate weight to each factor
e Converting factor maps format to raster
e Producing intermediate factor map
For example a geological map generalized into smaller number
of map units or classes. Also contact from the geological map is
selected and buffered, to produce aproximity map. Conceptual
modeling and knowledge driven, helps in data modeling,
selecting features to be enhanced and extracted as evidence
(factor), and deciding how to weight the relative importance of
evidence in estimating mineral potential. Interpretation of
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