LITHOLOGY AND SURFICIAL GEOLOGY MAPPING
Image credit: Arup
Near Surface Geology
Lithological features, lithology and surficial geology (soils) can be distinguished and mapped utilising a wide range of EO sensors and analytical techniques, often incorporating use of multiple EO datasets.
Products may include:
Products will vary according to particular user requirements and may range widely in geographic scale and level of detail and accuracy required.
Spectral analysis is an established key tool utilising spectral signatures of surface materials (using a variety of techniques including band-combination, band-ratio and PCA). Litho-types can be distinguished by spectral signature (of outcrop and/or vegetation) together with relationships with topography/geomorphology, in particular texture (surface roughness) and pattern (including drainage pattern). Altered rocks and soils (e.g. ferric iron alteration enrichment and clay mineral alteration) associated with onshore hydrocarbon seepage can provide important clues for basin geological modelling and exploration.
Multispectral analysis techniques use reflected infrared (VNIR, MIR) and thermal (emitted) wavelengths measured from a wide variety of sensors at different resolutions. Landsat and ASTER are widely utilised. Data fusion between different sensors and other datasets including DEM and geophysics allow for improved interpretation and lithological classification.
In addition to identification of broad litho-type groups, further distinction and characterisation can be made by remote sensing analysis including stratigraphic relationships (between litho-types) and tectonic relationships to further enhance geological modelling of the project area.
Geomorphological analysis including DEM and shaded-relief analysis can be used to identify soils by geomorphic form such as fluvial deposits (river terraces, alluvial fans, deltas) and sand dunes. Surface roughness and moisture content can provide an indication of degree of weathering and soil formation. High resolution DEM (e.g., derived from VHR1 sensors) are beneficial for mapping geomorphic form related to surficial deposits. Multi-temporal analysis can provide information on mobility of surficial deposits such as mobile dunes and fluvial (river/delta) systems.
Hyperspectral data can allow for distinction of finer levels of detail of spectral class and mineral identification allowing for more precise lithological differentiation, including variation within a formation unit (e.g., due to facies change, intrusions such as dykes and sills, hydrothermal alteration, weathering, duricrusts and hydrocarbon seepage). Future planned hyperspectral sensors, including EnMAP (2017), are anticipated to have good potential for lithological mapping.
Airborne geophysics data can be effectively incorporated with EO data analysis for more detailed and accurate lithological mapping. Spaceborne geophysics data currently is not at sufficient resolution to allow for detailed lithological distinction. Data collected from the GOCE satellite (2009-2013) has some benefit for mapping of global and broad regional scale geological structures including mapping depth of crust (depth to Moho) for input to broad regional seismic and tectonic modelling, including thermal gradient modelling.
Known restrictions / limitations
Lifecycle stage and demand
Pre-license: Information for geology to support decision-making on a prospect.
Exploration: Information to support geological mapping of surface and sub-surface, lithological and stratigraphic relationships, seep identification and seismic surveys (planning, e.g., trafficability, and data interpretation including seismic production modelling and near‑surface modelling).
Development: Information for planning and design of infrastructure, to support site selection and pipeline routing to determine hazards and risks in a proposed development area.
Production: Monitoring of changes in lithology/soils for asset monitoring of facilities and operations including pipeline leakage.
Decommissioning: Not typically required.
Geographic coverage and demand
Coverage is global.
Demand is global.
Demand is in all terrain areas, excluding polar and permanent snow covered landscapes.
OTM:014 Forecasting sand dune migration
OTM:023 Infrastructure planning
OTM:036 Geohazard exposure analysis
OTM:059 Understanding outcrop mineralogy
HC:2301 Identify discreet lithology
HC:2502 Identification of problem soils
Input data sources
Optical: VHR1, VHR2, HR1, HR2
Radar: VHR1, VHR2, HR1, HR2, MR1
Spatial resolution and coverage
Varies depending on input imagery used and client needs.
1:50,000 to 1:250,000 scale mapping is typical.
Minimum Mapping Unit (MMU)
Accuracy / constraints
Accuracy of interpretation is higher in arid and semi-arid regions. Temperate regions and tropical regions with thick soil cover and dense vegetation canopy have lower accuracy of interpretation.
Spectral libraries are inconsistent across differing geographic and terrain groups.
Thematic accuracy: 70-90% for arid/semi-arid regions where vegetation cover is low.
Spatial accuracy: The goal would be 1 pixel, but depends on reference data and ground-truth data.
Accuracy assessment approach & quality control measures
Professional judgment by comparison with any published geological mapping or reports and ground truth data (geological mapping and collection of field spectra, borehole logs).
Frequency / timeliness
Observation frequency: Typically only one date is required (per dataset/sensor used) and can frequently utilise archive data.
Timeliness of delivery: Depends on the requirements of the client and processing required. Archive data is frequently used and is usually available off-the-shelf.
Availability from commercial suppliers and other agencies.
New acquisitions can be requested globally for higher resolution data.
Archives products available for public search.
Delivery / output format
HC / Arup
OTM / WesternGeco
Lithology and Surficial Geology Mapping
# of Pages:
Internal – Project consortium and science partners
External – ESA
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