Download republic of indonesia ministry of energy and mineral resources

Prima M. Hilman
Head of Information Division – Centre for Geological Resources
REPUBLIC OF INDONESIA
MINISTRY OF ENERGY AND MINERAL RESOURCES
GEOLOGICAL AGENCY
Indonesia’s Mineral Potential
Law and Regulations concerning
Mineral Data and Information
Mineral Prospectivity Analysis Using GIS
Example of Methods
Conclusions
Lies on the three convergence of collided tectonic plates that created complex
geologic structures and various types of mineral deposits.
Highly mineralised archipelago (Rank 6th, 7th and 2nd in world copper, gold, and
nickel production, respectively).
A number of major discoveries have made Indonesia as one of highly
prospective region in the world (Rank 12th from 79 regions and 1st in AsiaPacific, Fraser Institute – 2011).
The country is still largely under-explored and will undoubtedly will produce
significant mineral deposits over the upcoming years.
New mining law in 2009 and mining regulations in 2010 foster mineral
development and foreign investment, simplify permitting process.
Article 87 affirms that to support the preparation of mining areas
and the development of mining science and technology, minister
or governor, in accordance with their authority, may assign state
and/or regional research agencies to perform mining surveys and
research.
Article 88 confirms that:
• Data obtained from mining business activities shall constitute state-owned
and/or region-owned in accordance with their authority.
• Mining business data owned by the regional government shall be required
to be reported to the state for national-level mining data management.
• Data as meant in paragraph (1) shall be managed by the state and/or
regional government in accordance with their authority.
In article 36 :
• State, province, and regency be obliged to manage mining data and/or
information accordance to their authority.
• Regional government have an obligation to send their mining data and/or
information to state.
• The result of data and/or information processing will be use in:
establishment of mineral and/or coal mining areas and their potential
classification; formation of mineral and coal resources and reserves balance
sheet; and development of mineral and coal science and technology.
In article 38 :
• Mining areas managed by Minister in the form of Mining Areas Information
System that nationally integrated.
• The system must be accessible by regional government.
WHAT IS PROSPECTIVITY MAPPING?
• Data generalisation / reduction technique
• Extracts strategic information from multiple exploration
datasets
• Identification of areas that:
• Fit pre-conceived models of deposit formation
• Are similar to areas known to contain significant
mineralisation
KNOWLEDGE-DRIVEN (CONCEPTUAL) APPROACH
• Application of concepts surrounding deposit
formation
• Representation of important factors in a spatial
context
• Combination of multiple factors into a single map
• Many models developed for deposit scale
• Difficult to apply regionally
DATA-DRIVEN (EMPIRICAL) APPROACH
• Identification of spatial relationships on a thematic
basis
• Proximity relationships
• size-proximity, strike-proximity, etc.
• Association relationships
• Abundance relationships
• Quantification of spatial relationships
• Integration of relationships
Geology
Geochemical
Geophysical
Remote Sensing
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Analyse
Combine
Regional Mineral
Exploration Data
Prospectivity Maps
• Frequency Ratio
• Weights of Evidence
• Fuzzy Logic
• Neural Networks
Regional probabilistic models of frequency ratio
and weight of evidence for base metal
mineralization in Painan, West Sumatra
• Analyze the relationships between base metal
mineralization and its related factors
• Integrate the relationships to identify areas that
have mineral potential nevertheless have not been
explored
• Define further exploration target
Painan and surrounding area, Western part of Sumatra Island (24,060 km2)
Spatial Database
• Geological Factor : lithology and fault
• Geochemical Factor : Ag, As, Cu, Fe, K, Li, Mn, Mo,
Pb, Sn, W, Zn.
Regional Geology and Mineralization
Geochemical Anomaly of Ag, As, Cu, and Fe
Geochemical Anomaly of K, Li, Mn, and Mo
Geochemical Anomaly of Pb, Sn, W, and Zn
Fault Buffering and Mineral Occurences
Frequency Ratio Models
• The ratio of the area where
mineral deposits occurred to
the total study area.
• That is the ratio of the
probabilities of a mineral
deposit occurrence to a nonoccurrence for a given
attribute.
• Therefore, the greater this
ratio is above unity, then the
stronger the relationship
between mineral deposit
occurrence and the given
factor’s attribute.
B
T
B∩D
B∩D
B∩D
D
B∩D
T : Total area
D : Area of deposits
B : Area of pattern
Weight of Evidence Models
• Binary maps showing areas of each factor were produced using
thresholds identified using the weights of evidence.
• To generate the binary predictor patterns of the factors, the
spatial database was reclassified into a binary pattern as
‘‘favorable’’ and the other formations as ‘‘non favorable’’.
Example of Coefficient for Geochemical Anomaly
Coefficient for Lithology
Coefficient for Faults
Data Integration
• Data integration resulting Mineralization Potential Index (MPI)
• Frequency ratio:
• MPI = FR
• FR = Frequency ratio of each factor’s range or type
• Weight of Evidence:
• MPI = WoE
• WoE = W+ and W- of binary pattern of each factor’s range or type
MINERALIZATION POTENTIAL INDEX MAP IN FREQUENCY RATIO MODEL
MINERALIZATION POTENTIAL INDEX MAP IN WEIGHT OF EVIDENCE MODEL
Verification
• The mineral potential maps were verified using existing
mineral deposits.
• The verification method was performed by comparison of
existing mineral deposit and mineral potential maps using
success rate method.
• To compare the result quantitative, the areas under the curve
were re-calculated as the total area is 1 which means perfect
prediction accuracy.
Verification of Frequency Ratio Model
40 precious-base metals mineralization, 25 points for processing and 15 points for test (verification)
• Verification of frequency ratio
model
using
testing
mineralization points; the
area ratio was 0.7868 and the
prediction accuracy is 78.68%.
• Verification using processed
mineralization points itself ;
the area ratio was 0.9580 and
the prediction accuracy is
95.80%.
Verification of Weight of Evidence Model
40 precious-base metals mineralization, 25 points for processing and 15 points for test (verification)
• Verification of weight of
evidence model using testing
mineralization points; the area
ratio was 0.6821 and the
prediction accuracy is 68.21%.
• Verification using processed
mineralization points itself ; the
area ratio was 0.8038 and the
prediction accuracy is 80.38%.
Delineation of Prospective Area for Base Metal Mineralization
Regional probabilistic models of frequency ratio and weight of evidence are a
useful technique to evaluate the mineral potential.
Frequency ratio model showed the higher accuracy than weight of evidence
model.
Base metal mineralization potential index map give the guidance to delineate
the prospective areas in order to prepare new mining permit zones.
REPUBLIC OF INDONESIA
MINISTRY OF ENERGY AND MINERAL RESOURCES
GEOLOGICAL AGENCY
http://www.bgl.esdm.go.id