GIS Fundamentals
Basic Concepts and Components
A Geographic Information System (GIS) is a computer-based system for capturing, storing, analyzing, and displaying geographically referenced data. It brings together hardware, software, databases, and trained personnel to enable spatial analysis and mapping. In geophysics, GIS serves as the central platform where you pull together diverse datasets (seismic surveys, gravity measurements, satellite imagery) and analyze them in a shared spatial framework.
Every GIS workflow moves through four main stages:
- Data input — getting information into the system through digitizing, scanning, or GPS collection
- Data storage and management — organizing data in databases (often geodatabases) so it can be efficiently queried and updated
- Data manipulation and analysis — performing operations like overlay, buffering, and interpolation to extract meaning from the data
- Data output — producing maps, reports, 3D visualizations, or interactive displays that communicate results
Data Organization and Models
GIS data is organized into layers, where each layer represents a specific theme or attribute (elevation, land use, geophysical properties, etc.). You stack and combine these layers to reveal spatial relationships. For example, overlaying a gravity anomaly layer on a geological map layer can highlight where subsurface structures correlate with known rock types.
Two primary data models are used:
- Vector (points, lines, and polygons) — best for discrete features like well locations (points), fault traces (lines), or survey boundaries (polygons)
- Raster (grid cells) — best for continuous phenomena like elevation, temperature, or magnetic field intensity. Each cell holds a single value, and the grid covers the entire area
Coordinate systems and map projections define the spatial reference framework for all data in a GIS. Because the Earth is roughly spherical but maps are flat, projections introduce distortions in area, shape, distance, or direction. Choosing the right projection matters: a poor choice can misalign datasets or distort spatial measurements. All layers in a GIS project need to share a common coordinate system, or at minimum be properly reprojected, so they line up correctly.
GIS for Geophysical Data
Data Management and Analysis
Geophysical projects generate large volumes of spatially referenced data: seismic surveys, well logs, gravity and magnetic measurements, electromagnetic soundings, and more. GIS provides the infrastructure to store this data in geodatabases, which enable efficient querying, updating, and sharing across research teams.
Spatial analysis techniques in GIS are especially valuable for estimating geophysical properties at locations where no measurements exist. Interpolation fills in the gaps between sampled points to create continuous surfaces. Kriging, a geostatistical interpolation method, is particularly common in geophysics because it not only estimates values but also quantifies the uncertainty of those estimates based on the spatial structure of the data.
GIS also enables overlay analysis, where you combine multiple geophysical datasets to look for correlations. A classic example: overlaying gravity anomaly data with magnetic anomaly data can help identify subsurface structures (like intrusions or basins) that produce signatures in both fields simultaneously.
Geostatistics and Visualization
Geostatistical tools built into GIS platforms help you assess the spatial dependence and variability of geophysical data before and after interpolation:
- Variograms model how data similarity changes with distance, which is essential for setting up kriging parameters correctly
- Spatial autocorrelation analysis quantifies whether nearby measurements are more similar than distant ones, aiding in data quality control and uncertainty assessment
GIS provides a wide range of visualization options for geophysical data:
- 2D maps — contour maps of gravity or magnetic anomalies, color-ramped surfaces of interpolated properties
- 3D models — subsurface block models integrating seismic reflection data with well log data
- Cross-sections — vertical slices through subsurface models showing layered structures
- Interactive displays — allowing users to rotate, zoom, and query features dynamically
These visualizations aren't just for presentation. They're active tools for data exploration, helping you spot patterns, anomalies, or errors that raw numbers alone won't reveal.
Remote Sensing Integration in GIS
Data Integration and Georeferencing
Remote sensing data (satellite imagery, airborne geophysical surveys, LiDAR) provides information about the Earth's surface and, in some cases, the shallow subsurface. GIS serves as the platform where you integrate this remote sensing data with ground-based measurements and other geospatial datasets.
Georeferencing is the process of assigning real-world coordinates to a remote sensing image so it aligns accurately with other GIS layers. This typically involves identifying ground control points (GCPs), which are locations visible in both the image and a reference dataset with known coordinates. Without proper georeferencing, overlaying remote sensing data on other layers produces misaligned, unreliable results.
Feature Extraction and Spectral Analysis
Once remote sensing data is georeferenced and loaded into GIS, you can extract geologically meaningful features from it:
- Lineaments (linear features that may indicate faults or fracture zones) can be digitized as vector lines and compared against geophysical anomaly maps
- Geomorphological patterns like drainage networks or landforms can be vectorized and analyzed for structural controls
Spectral analysis techniques within GIS enhance specific properties in multi-spectral remote sensing data:
- Band ratios divide one spectral band by another to highlight mineralogical differences (e.g., iron oxide vs. clay content)
- Principal component analysis (PCA) reduces redundant spectral information into a smaller number of components that capture the most variance, making subtle features more visible
Data fusion techniques like pan-sharpening combine high-resolution panchromatic (single-band) imagery with lower-resolution multi-spectral data. The result is an image with both high spatial resolution and rich spectral information, useful for detailed geological mapping.
GIS Mapping and Modeling for Geophysics
Thematic Mapping and Data Integration
GIS enables the creation of publication-quality, interactive maps that communicate geophysical data and interpretations clearly. Thematic mapping techniques include:
- Color ramping — assigning a gradient of colors to a range of values (e.g., blue-to-red for low-to-high Bouguer gravity anomaly)
- Data classification — grouping continuous values into discrete classes to highlight patterns or anomaly thresholds
A major strength of GIS is the ability to integrate geophysical data with contextual information like geological maps, well locations, roads, and infrastructure. This provides a comprehensive spatial context that supports more informed interpretation and planning.
3D Modeling and Decision Support
3D modeling capabilities in GIS allow you to build subsurface models by integrating multiple data types:
- Start with surface geophysical data (gravity, magnetics, seismic reflection profiles)
- Incorporate borehole data (lithology logs, geophysical well logs) as vertical constraints
- Interpolate between data points to generate 3D volumes representing subsurface structures, reservoir properties, or fluid flow pathways
Multi-criteria decision analysis (MCDA) is a GIS-based approach that supports complex decision-making. It works by integrating and weighting various factors (geophysical anomaly strength, geological favorability, environmental constraints, infrastructure access) to produce a composite suitability map. This is commonly used for site selection in mineral exploration, geothermal development, or survey planning.
Web-based GIS platforms and cloud computing have expanded collaboration in geophysical projects. Teams can share maps, models, and raw data through online portals, making it possible for researchers in different locations to access and contribute to the same spatial datasets in near real-time.