Overview
The Multivariate Anomaly Map module, part of the Data Augmentation suite, identifies spatial anomalies across multiple geoscientific variables using pixel-based raster analysis. Unlike traditional methods that detect anomalies at individual sample points, this module works on continuous grids, enabling consistent spatial comparison across a defined Area of Interest (AOI).
By integrating multi-element geochemical or multi-parameter geophysical inputs, the module highlights enrichment and depletion zones that may indicate mineralization or alterations. It is particularly effective in porphyry-style systems, where large-scale, multi-element anomalies are common. While applicable to a range of deposit types, its key strength lies in detecting complex, overlapping signals often missed in single-variable analysis.
Topic | Summary |
Module Name | Multivariate Anomaly Maps |
Purpose | Identifies spatial anomalies across multiple variables to reveal enrichment and depletion patterns related to mineralization. |
Input Format | Raster |
Recommended Data | Multi-element geochemical rasters (e.g., from soils, tills, or rocks) or multi-channel geophysical grids (e.g., EM, gravity, or magnetics), (multi-channel satellite imagery grids (Sentinel, Aster) |
Output Format | Raster; Feature Importance graph |
Key Parameters | AOI, grid resolution, selection and grouping of input rasters by geochemical behaviour |
Processing Summary | Applies a multivariate Isolation Forest to input rasters to generate anomaly and feature importance outputs. |
Typical Use Cases | Detecting geochemical or geophysical anomalies |
Validation or QC | Not applicable (due to the statistical and predictive nature of the process) |
Common Pairings | Gridded Maps; EM Channels; imagery bands |
Notable Output Notes |
|
How It’s Used in Exploration
The Multivariate Anomaly Map module is commonly used to generate spatial anomaly layers from stacked geochemical or geophysical grids. It’s particularly valuable for screening large areas where multiple variables must be compared consistently across space.
In geochemical workflows, the module is typically applied to till or soil data that has been interpolated into rasters. Grouping elements by geochemical behaviour enables detection of multi-element clusters associated with hydrothermal systems or dispersion patterns. The same approach can be used with gridded geophysical data to highlight subtle patterns that may reflect structural or lithologic boundaries.
This workflow supports multi-layer input, allowing up to 50 geophysical grids to be combined into a single filtered output. This is especially useful when characterizing areas with overlapping physical signals — such as magnetic, gravity, or resistivity data collected across different campaigns — where integrated analysis helps reveal consistent trends.
The anomaly output can guide targeting decisions, support interpretation during field planning, or be used in DORA’s Prediction Map workflow. For best results, outputs should be evaluated in context and supported by ground validation where possible.
Value and Benefits
The Multivariate Anomaly Map module provides a repeatable, data-driven method for detecting geochemical or geophysical outliers across a study area. By analyzing all input layers simultaneously, it simplifies interpretation and reduces the risk of overlooking subtle patterns that may indicate mineralization or alteration.
The output is a single raster layer that condenses complex, multi-variable datasets into a usable format for early-stage targeting or for use in DORA’s Prediction Map workflow. It is especially valuable when working with multi-element soil or till data, or combining multiple geophysical datasets in data-rich regions.
When used thoughtfully, this module helps teams identify and prioritize areas with the greatest exploration potential.
Learn More
Still Have Questions?
Reach out to your dedicated DORA contact or email support@VRIFY.com for more information.