Overview
The Lineament Maps Module, part of the Data Augmentation suite, automatically detects and maps linear features from raster datasets such as magnetics, gravity, resistivity, or LiDAR-derived elevation. These lineaments often represent faults, shear zones, or geological boundaries inferred from breaks or contrasts in geophysical or topographic data.
This method replaces manual digitization in GIS, offering a faster and more objective approach to structural interpretation. It is especially valuable in orogenic gold settings where surface structures are often obscured by overburden, lakes, or glacial cover. The Module produces two key raster outputs: Line Density Maps and Structural Complexity Maps, both of which support downstream interpretation and targeting in DORA or standalone workflows.
Topic | Summary |
Module Name | Lineaments Maps |
Purpose | Extracts and maps linear features from raster data using edge and line detection |
Input Format | Raster |
Recommended Data | Any geoscientific data |
Output Format | Raster |
Key Parameters | Grid resolution, AOI, edge detection sensitivity, minimum line length, minimum gaps, lineament orientation filters |
Processing Summary | Applies edge detection followed by line detection; optional orientation filtering |
Typical Use Cases | Structural interpretation, targeting structurally-controlled mineralization zones |
Validation or QC | Not applicable (feature extraction; visual review recommended) |
Common Pairings | Fault Disturbance Maps, Structural Field Maps, Distance Maps |
Notable Output Notes |
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How It’s Used in Exploration
Lineament Extraction is often used early in structurally focused exploration workflows. It helps geologists interpret structure in areas where mapping is limited due to cover, poor exposure, or limited historical data. The Module analyzes raster datasets to identify orientation trends based on contrast boundaries, such as where magnetic highs meet lows or where topographic features change abruptly.
In orogenic gold systems, faults and shear zones are commonly inferred from geophysics. This Module automates that inference, producing consistent results across the entire Area of Interest. The outputs are used to support targeting in structurally controlled deposits by highlighting areas of high lineament concentration or complexity.
The method is also applicable to topographic data, such as LiDAR and satellite-derived DEM products in glaciated terrain, where weathering between rock types can create surface expressions that reflect buried structures. In these settings, the Module can help detect faults, alteration zones, or shear corridors where traditional mapping may not be possible.
The Lineament Density Map shows where structural features are concentrated, while the Structural Complexity Map identifies areas where multiple orientations intersect or change direction. These layers are commonly used as inputs for other Modules, such as Distance Maps or Fault Disturbance Maps, or interpreted on their own in early-stage targeting.
Value and Benefits
Lineament Extraction provides a repeatable and unbiased method for structural mapping using standard geophysical or topographic data. It reduces reliance on manual digitization, which is time-consuming and varies by interpreter. Instead, the Module applies a consistent process across the AOI, improving the reliability of structural layers used in targeting and modelling.
The two output grids (rasters), line density and structural complexity, are particularly useful in identifying areas of deformation or structural convergence, which are often key to fluid flow and mineralization in structurally controlled systems. These outputs can highlight new areas of interest that may have been overlooked in manual interpretation.
Because the method is based on pixel-scale analysis of gradients and edges, results are sensitive to input data quality and resolution. High-quality, edge-enhanced datasets tend to produce the most meaningful outputs. As with any feature extraction tool, outputs should be interpreted in geological context, ideally supported by field data or expert review.
This Module supports a more systematic and objective approach to structural analysis, helping exploration teams make faster, more informed decisions about where to focus their efforts.
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Still have questions?
Reach out to your dedicated DORA contact or email support@VRIFY.com for more information.