Model-based Spatial Data integration

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

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.

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?

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 spatial proxies 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

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) MODELS – Empirical Models

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.

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

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

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

Hybrid models Adaptive fuzzy inference systems

Input data preparation

SCALE Ligand source Metal source Model I Model II Model III Trap Region Energy (Driving Force) Transporting fluid Residual Fluid Discharge Mineral System (≤ 500 km) Deposit Halo Deposit (≤ 10 km) (≤ 5 km) COMPONENTS 1. Energy2. Ligand3. Source4. Transport5. Trap 6. Outflow INGREDIENTS Deformation Metamorphism Magmatism Connate brines Magmatic fluids Meteoric fluids Enriched source rocks Magmatic fluids Structures Permeable zones Structures Chemical traps Structures aquifers MAPPABLE CRITERIA Link processes to predictor maps Metamorphic grade, igneous intrusions, sedimentary thickness 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 SPATIAL PROXIES

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.

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)

Geology

Structures

Geochemistry

MAGNETIC DATA

GRAVITY DATA

Gamma-ray Spectrometric data

LANDSAT TM data

Predictor maps ProcessPossible predictor map(s)GIS processing Energy for driving fluid circulation Map of granitesQuerying geological map for granites and associated igneous rocks; Extraction Map of metamorphic gradesQuerying geological maps for specific metamorphic minerals that indicate the grade of metamorphism; Reclassification Isopach map of sedimentary rocksInterpolation of sediment thickness in boreholes Ligand source Presence of evaporite (mainly halites) diapirs Querying geological map for halites/salt domes/evaporites; Extraction Metal source Map of granitesQuerying geological map for granites and associated igneous rocks; Extraction; Euclidean distance calculation Pathways Proximity to faultsQuerying for faults, Euclidean distance calculation Proximity to lineamentsQuerying for lineaments, distance calculation Physical traps Proximity to fold axesQuerying for fold axes, Euclidean distance calculation High fault densityLine density estimation High fault intersection densityExtraction of fault intersections, point density estimations High geological contact densityLine density estimation High competency contrast across geological contacts Assign rheological strength values to all rocks on the geological map, Assign rheological difference values across each geological contact to the geological contact;

Predictor maps ProcessPossible predictor map(s)GIS processing Chemical traps Map of Chemical reactivity (Fe anomalies)Interpolation of Fe values from geochemical data Gold anomaliesInterpolation of Au values from geochemical data As, Sb, Cu, Bi anomaliesInterpolation of Au values from geochemical data

Energy source/Metal source: Distance to granites

Pathways: Distance to Faults

Physical trap: Fault density

Physical trap: Fault intersection density

Physical trap: Competency contrast

Chemical trap: Fe Concentration

Chemical trap: As Concentration

Chemical trap: Sb Concentration

Chemical trap: Au Concentration

Gold deposits