1. Prima M. Hilman Head of Information Division – Centre for Geological Resources REPUBLIC OF INDONESIA MINISTRY OF ENERGY AND MINERAL RESOURCES GEOLOGICAL AGENCY

2. Indonesia’s Mineral Potential Law and Regulations concerning Mineral Data and Information Mineral Prospectivity Analysis Using GISExample of MethodsConclusions

3. Lies on the three convergence of collided tectonic plates that created complex geologic structures and various types of mineral deposits. Highly mineralised archipelago (Rank 6 th, 7 th 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 12 th from 79 regions and 1 st in Asia- Pacific, 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.

5. 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.

6. 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.

7. 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

8. 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

9. 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

10. Geophysical Prospectivity Maps Regional Mineral Exploration Data GIS Analyse Combine Analyse Combine Frequency Ratio Weights of Evidence Fuzzy Logic Neural Networks Frequency Ratio Weights of Evidence Fuzzy Logic Neural Networks Geology Geochemical Remote Sensing

11. 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

12. Painan and surrounding area, Western part of Sumatra Island (24,060 km 2 )

13. Spatial Database Geological Factor : lithology and fault Geochemical Factor : Ag, As, Cu, Fe, K, Li, Mn, Mo, Pb, Sn, W, Zn.

14. Regional Geology and Mineralization

15. Geochemical Anomaly of Ag, As, Cu, and Fe

16. Geochemical Anomaly of K, Li, Mn, and Mo

17. Geochemical Anomaly of Pb, Sn, W, and Zn

18. Fault Buffering and Mineral Occurences

19. 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 non- occurrence 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∩DB∩DB∩DB∩D B ∩ D B∩DB∩DB∩DB∩DD B∩DB∩DB∩DB∩D B T T : Total area D : Area of deposits B : Area of pattern

20. 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’’.

21. Example of Coefficient for Geochemical Anomaly

22. Coefficient for Lithology

23. Coefficient for Faults

24. 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

25. MINERALIZATION POTENTIAL INDEX MAP IN FREQUENCY RATIO MODEL

26. MINERALIZATION POTENTIAL INDEX MAP IN WEIGHT OF EVIDENCE MODEL

27. 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.

28. Verification of Frequency Ratio Model 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%. 40 precious-base metals mineralization, 25 points for processing and 15 points for test (verification)

29. Verification of Weight of Evidence Model 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%. 40 precious-base metals mineralization, 25 points for processing and 15 points for test (verification)

30. Delineation of Prospective Area for Base Metal Mineralization

31. 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.

32. REPUBLIC OF INDONESIA MINISTRY OF ENERGY AND MINERAL RESOURCES GEOLOGICAL AGENCY http://www.bgl.esdm.go.id