Model-based Spatial Data integration

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# Model-based Spatial Data integration - PowerPoint PPT Presentation

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)

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## Model-based Spatial Data integration

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Presentation Transcript
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 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
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)
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

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

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)

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