Remote Sensing for Mineral Exploration Training Course

Participants can match sensor characteristics to geological questions and target scales.

• Spatial, spectral, temporal and radiometric resolution compared for exploration tasks
• Landsat, Sentinel-2 and ASTER capabilities for regional geological mapping
• Hyperspectral imagery applications for mineral identification and alteration discrimination
• Sentinel-1 radar responses to terrain, roughness, moisture and structure
• Digital elevation model sources for landform and lineament interpretation
• Sensor-selection matrix based on target minerals, cover and terrain

Participants can establish a controlled GIS workspace for integrated exploration datasets.

• Coordinate reference systems selected for regional and prospect-scale mapping
• Datum transformations checked before combining legacy exploration datasets
• Raster cell size aligned with sensor resolution and analysis purpose
• Vector data schemas for geology, samples, structures and target polygons
• Metadata fields recording source, processing history, date and limitations
• File naming conventions supporting audit trails and team handover

Participants can prepare optical imagery for reliable visual and quantitative interpretation.

• Radiometric calibration converting digital numbers into physically meaningful reflectance
• Atmospheric correction methods for surface reflectance and scene comparability
• Cloud, haze and shadow masks applied before spectral analysis
• Image mosaics balanced across dates, sensor scenes and illumination conditions
• Geometric registration assessed using control points and displacement checks
• Quality-control log documenting preprocessing decisions, parameters and residual limitations

Participants can prepare radar and terrain data for structural and geomorphological interpretation.

• Synthetic aperture radar geometry explained through incidence angle and layover
• Speckle filtering selected without removing structurally relevant image texture
• Terrain correction applied to radar scenes using elevation models
• Hillshades generated under multiple azimuths for lineament visibility
• Slope, aspect and curvature derivatives used for landform interpretation
• Radar backscatter limitations assessed in vegetation, sand and steep terrain

Participants can map geological units and contacts using visual image interpretation criteria.

• Interpretation keys built from tone, texture, pattern, shape and context
• False-colour composites selected for lithological contrast and vegetation suppression
• Drainage patterns interpreted for lithology, structure and permeability differences
• Geomorphological surfaces separated from bedrock signatures and transported cover
• Lithological contacts mapped with confidence classes and supporting observations
• Image interpretation polygons reconciled with published geology and field records

Participants can interpret spectral absorption features relevant to alteration minerals.

• Electromagnetic spectrum regions linked to iron, clay, carbonate and hydroxyl minerals
• Diagnostic absorption features compared using laboratory and image spectra
• USGS Spectral Library records selected for plausible mineral assemblages
• Continuum removal used to compare absorption depth and feature position
• Mixed-pixel effects recognised in weathered, vegetated and heterogeneous terrain
• Spectral interpretation constrained by mineral paragenesis and deposit models

Participants can apply band ratios and indices to highlight alteration-related materials.

• Iron oxide ratios developed from visible and near-infrared bands
• Clay and hydroxyl proxies derived from shortwave infrared band combinations
• Carbonate responses separated cautiously from dry soil and altered rock
• Vegetation indices used to mask biologically dominated spectral signals
• Ratio images stretched using histograms, percent clips and density slices
• Ratio anomalies checked against lithology, terrain and acquisition conditions

Participants can use multivariate methods to expose spectral patterns in multispectral imagery.

• Principal component analysis separates correlated spectral information among bands
• Crosta technique applied to highlight iron oxide and hydroxyl responses
• Minimum noise fraction transformation used before hyperspectral feature extraction
• Decorrelation stretch enhances subtle colour differences between surface materials
• Training data quality assessed before supervised image classification
• Classification outputs validated using reference points and confusion matrices

Participants can produce alteration maps linked to mineral system vectors and uncertainty.

• Alteration assemblages related to potassic, phyllic, argillic and propylitic zones
• Porphyry copper vectors interpreted from alteration patterns and structural settings
• Epithermal alteration signatures distinguished from surficial clay accumulations
• Iron staining mapped separately from primary hydrothermal mineralisation evidence
• Alteration domains digitised with confidence, source and validation attributes
• Field-verification priorities assigned using geological significance and image uncertainty

Participants can identify and assess structures relevant to fluid flow and target generation.

• Lineaments interpreted from hillshades, radar textures and optical imagery
• Multi-azimuth hillshades used to reduce directional illumination bias
• Fault traces distinguished from lithological contacts, roads and drainage lines
• Rose diagrams summarise orientation sets and structural domain patterns
• Lineament density calculated within geologically meaningful analysis windows
• Structural intersections ranked using displacement, continuity and deposit-model relevance

Participants can account for regolith and surface processes when interpreting exploration imagery.

• Regolith-landform units mapped from terrain, drainage and spectral characteristics
• Transported cover thickness inferred cautiously from landform and outcrop patterns
• Lateritic surfaces recognised as potential sources of spectral confusion
• Alluvial, colluvial and aeolian materials separated for sampling interpretation
• Drainage catchments delineated for reconnaissance geochemical survey planning
• Surface-process interpretations documented with confidence and verification requirements

Participants can integrate image interpretation with geology and geochemical evidence in GIS.

• Geological maps harmonised with interpreted units and structural traces
• Rock-chip and soil geochemistry displayed using appropriate symbolisation and thresholds
• Geochemical anomalies assessed against catchments, regolith and lithological controls
• Spatial joins link sample results with alteration and lineament attributes
• Interpolation limitations considered before using gridded geochemical surfaces
• Contradictory datasets recorded as uncertainty rather than forced agreement

Participants can combine remotely sensed evidence with geophysical datasets for target appraisal.

• Magnetic derivatives compared with mapped structures and lithological boundaries
• Radiometric potassium, thorium and uranium patterns assessed with surface geology
• Gravity anomalies considered alongside density contrasts and geological models
• Electromagnetic conductors screened against alteration, structure and cultural noise
• Raster transparency and blend methods used for multi-layer visual comparison
• Integrated evidence maps retain source resolution and interpretation limitations

Participants can construct a transparent prospectivity model from deposit-model evidence layers.

• Knowledge-driven prospectivity models structured around source, pathway and trap criteria
• Favourability layers standardised using ordinal scores and documented thresholds
• Weighted linear combination applied with explicit geological rationale
• Analytic hierarchy process used to compare evidence-layer relative importance
• Boolean models contrasted with continuous favourability and uncertainty surfaces
• Model assumptions tested against known occurrences and withheld reference data

Participants can rank exploration targets while making uncertainty visible to decision makers.

• Target criteria defined from deposit models, access and exploration objectives
• Evidence tables distinguish observations, interpretations, assumptions and unknowns
• Sensitivity analysis tests weight changes within prospectivity model scenarios
• Uncertainty surfaces identify areas affected by cover, shadows and sparse data
• Target ranks separated into follow-up, watchlist and exclusion categories
• Decision gates specify information required before progressing target expenditure

Participants can plan field verification that tests key remote-sensing interpretations efficiently.

• Verification traverses prioritised around high-value assumptions and accessible exposures
• Observation forms capture lithology, alteration, structure and spectral context
• Field photographs geotagged with direction, scale and observation identifiers
• Portable spectrometer readings collected with calibration and reference procedures
• Representative samples selected for petrography, geochemistry and mineralogical confirmation
• Field results fed back into interpretation polygons and confidence ratings

Participants can communicate remote-sensing results through review-ready maps and technical reports.

• Map layouts show data sources, processing methods, scale and coordinate information
• Symbology distinguishes measured data, interpreted features and inferred boundaries
• Target sheets summarise rationale, evidence, uncertainty and recommended actions
• Processing flowcharts document reproducible steps, parameters and software versions
• Technical language separates observed spectral response from geological interpretation
• Peer-review checklists test traceability, clarity and decision relevance

Participants can plan responsible use, storage and review of exploration remote-sensing data.

• Data licences checked before sharing imagery, derivatives and interpretation products
• Indigenous land, protected areas and access constraints recorded in planning layers
• Sensitive target information controlled through permissions and version management
• Processing records retained for internal review and external technical assurance
• Environmental disturbance minimised when planning verification traverses and sample sites
• Escalation procedures defined for conflicting datasets and material target revisions

Participants can build, review and present a complete remote-sensing-supported mineral exploration target study.

• Deposit model selected to frame target criteria and evidence requirements
• Satellite, terrain, geology, geochemistry and geophysics layers assembled coherently
• Processing workflow documented from raw imagery through interpretation outputs
• Alteration, structure and lithological evidence combined within a prospectivity model
• Target ranking justified through uncertainty analysis and field-verification priorities
• Technical review presentation communicates maps, rationale, limitations and next actions