Model-based Spatial Data integration
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Transcript of Model-based Spatial Data integration
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Model-based Spatial Data integration
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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
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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.
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MODELS – Stochastic ModelsOUTPUT 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?
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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
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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
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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.
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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
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Knowledge-driven model
• Boolean overlay• Index overlay• Fuzzy set theroy• Dempster-shafer belief theory
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Data-driven model
• Bayesian Probabilistic (weights of evidence)• Logistic regression• Artificial neural networks
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Hybrid models
• Adaptive fuzzy inference systems
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Input data preparation
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SCALE
Ligand source
Metal source
Model I
Model II
Model III
Trap Region
Energy(Driving Force)
Transporting fluid
ResidualFluid Discharge
Mineral System(≤ 500 km) Deposit Halo
Deposit(≤ 10 km)
(≤ 5 km)
COMPONENTS 1. Energy 2. Ligand 3. Source 4. Transport 5. Trap 6. Outflow
INGR
EDIE
NTS
Deformation MetamorphismMagmatism
Connate brinesMagmatic fluidsMeteoric fluids
Enriched source rocksMagmatic fluids
Structures Permeable zones
Structures Chemical traps
Structures aquifers
MAP
PABL
E CR
ITER
IA
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
SPAT
IAL
PROX
IES
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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.
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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)
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Geology
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Structures
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Geochemistry
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MAGNETIC DATA
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GRAVITY DATA
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Gamma-ray Spectrometric data
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LANDSAT TM data
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Predictor maps
Process Possible predictor map(s) GIS processingEnergy for driving fluid circulation
Map of granites Querying geological map for granites and associated igneous rocks; Extraction
Map of metamorphic grades Querying geological maps for specific metamorphic minerals that indicate the grade of metamorphism; Reclassification
Isopach map of sedimentary rocks Interpolation 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 granites Querying geological map for granites and associated igneous rocks; Extraction; Euclidean distance calculation
PathwaysProximity to faults Querying for faults, Euclidean distance calculationProximity to lineaments Querying for lineaments, distance calculation
Physical traps
Proximity to fold axes Querying for fold axes, Euclidean distance calculationHigh fault density Line density estimationHigh fault intersection density Extraction of fault intersections, point density estimationsHigh geological contact density Line density estimationHigh 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;
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Predictor maps
Process Possible predictor map(s) GIS processingChemical traps
Map of Chemical reactivity (Fe anomalies) Interpolation of Fe values from geochemical data
Gold anomalies Interpolation of Au values from geochemical data
As, Sb, Cu, Bi anomalies Interpolation of Au values from geochemical data
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Energy source/Metal source:Distance to granites
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Pathways:Distance to Faults
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Physical trap:Fault density
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Physical trap:Fault intersection density
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Physical trap:Competency contrast
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Chemical trap:Fe Concentration
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Chemical trap:As Concentration
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Chemical trap:Sb Concentration
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Chemical trap:Au Concentration
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Gold deposits