Model-based Spatial Data integration

1. Model-based Spatial Data integration

2. MODELS OUTPUT MAP = ∫ (Two or More Maps) • The integrating function is estimated using either: • Theoretical understanding of physical and chemical principles, or • Based on observational data

3. MODELS – Deterministic Models OUTPUT MAP = ∫ (Two or More Maps) For example, you want to derive a map of water circulation in a lake: • Velocity field = ∫ water depth, bottom slope, inflow, outflow, wind orientation and direction • Apply Navier-Stokes equation to get the output.

4. MODELS – Stochastic Models OUTPUT MAP = ∫ (Two or More Maps) • For example, you want to derive a map of groundwater potential in an area. Conceptually, we can say: Ground water potential = ∫ Ground water recharge, discharge • And further: Groundwater recharge = ∫ availability of water for recharging; percolation of water to the aquifers Ground water discharge = ∫evapo-transpiration; extraction for human use. • There are no theoretical equation available to combine these maps. • So what do we do?

5. MODELS – Empirical Models • Groundwater recharge = ∫Water availability, Water percolation • Water availability – how do we map? • Rainfall maps • Wide rivers in late stage, proximity to rivers • Percolation • water flow velocity – we use slope maps • drainage density, • Landuse • soil and rock permeability, • structural permeability – density of faults, joints; proximity to faults etc • These maps are said to serve as spatialproxies for the two factors, water availability and water percolation. We call them predictor maps. • Groundwater discharge= ∫Evapo-transpiration, extraction humans • Proxies for evapo-transpiration: • Vegetation density • Humidity distribution • Wind velocity • Temperature distribution • Agriculture intensity • Population density

6. MODELS – Empirical Models Now we redefine the model in terms of proxies. • Ground water potential = ∫ Ground water recharge, discharge • = ∫ (Rainfall maps, proximity to rivers, slope maps, drainage density, land-use, soil and rock permeability, density of faults, joints; proximity to faults etc) • AND • (vegetation density, humidity distribution, wind velocity, temperature distribution, agriculture intensity, population density)

7. How do derive output groundwater potential map? • We combine the proxies or predictor maps • We can overlay the above maps in a simplistic way, and add them up. • But the problem is, all the factors do not contribute equally to water recharge, do they? • So we need to provide weights before combining them.

8. How do derive the output groundwater potential map? • We can either assign weights based on our Knowledge about groundwater recharge/discharge Or we can use empirical observations to determine the weights. The empirical observations are used as training points. • Based on whether we use our knowledge to assign weight to the map, or we use empirical observation to determine the weights, we call a model knowledge-driven or data-driven. • A third category of models are called hybrid models, which use both knowledge and data

9. Knowledge-driven model • Boolean overlay • Index overlay • Fuzzy set theroy • Dempster-shafer belief theory

10. Data-driven model • Bayesian Probabilistic (weights of evidence) • Logistic regression • Artificial neural networks

11. Hybrid models • Adaptive fuzzy inference systems

12. Input data preparation

13. Link processes to predictor maps 1. Energy 2. Ligand 3. Source 4. Transport 5. Trap 6. Outflow COMPONENTS Mineral System SCALE (≤ 500 km) Deposit Halo (≤ 10 km) Deposit (≤ 5 km) Model I Metal source Transporting fluid INGREDIENTS Model II Ligand source Energy(Driving Force) Model III ResidualFluid Discharge Trap Region Deformation MetamorphismMagmatism Connate brinesMagmatic fluidsMeteoric fluids Enriched source rocksMagmatic fluids Structures Permeable zones Structures Chemical traps Structures aquifers MAPPABLE CRITERIA Evaporites, Organics, isotopes Radiometric anomalies, geochemical anomalies, whole-rock geochemistry Fault/shear zones, folds geophysical/ geochemical anomalies, alteration Dilational traps, reactive rocks, geophyiscal/ geochemical anomalies, alteration magnetic/ radiometric/ geochemical anomalies, alteration, structures Metamorphic grade, igneous intrusions, sedimentary thickness SPATIAL PROXIES

14. Predictor maps • A GIS data layer that can predict the presence of a mineral deposit is called a predictor map. • Also called evidential maps because they provide spatial evidence for processes that form mineral deposits.

15. Primary datasets typically available for mineral exploration • Geological map (rocks types, rock description, stratigraphic groupings; typically vector polygon map + attribute table) • Structural maps (type of structures e.g., Faults, folds, joints, lineament etc; typically vector line map + attribute table) • Geochemical maps (multi-element concentration values at irregularly distributed sample locations + attribute table) • Geophysical images (gravity and magnetic field intensity, ratser images, no attribute tables) • Remote sensing images (multispectral/hyperspectral, no attribute tables)

16. Geology

17. Structures

18. Geochemistry

19. MAGNETIC DATA

20. GRAVITY DATA

21. Gamma-ray Spectrometric data

22. LANDSAT TM data

23. Predictor maps

24. Predictor maps

25. Energy source/Metal source: Distance to granites

26. Pathways: Distance to Faults

27. Physical trap: Fault density

28. Physical trap: Fault intersection density

29. Physical trap: Competency contrast

30. Chemical trap: Fe Concentration

31. Chemical trap: As Concentration

32. Chemical trap: Sb Concentration

33. Chemical trap: Au Concentration

34. Gold deposits