Overview
Feature Processing transforms raw geological, geochemical, and geophysical data into continuous grids that support exploration analysis and modelling, including DORA.
The process begins when VRIFY receives project-specific datasets and compiles relevant regional public data. Inputs may include soil samples, rock geochemistry, geophysical layers, structural measurements, and other available exploration data.
VRIFY’s team of geoscientific experts review the incoming data and feeds it into one or more of the ten specialized Feature Processing Modules, depending on the nature of the inputs and the exploration objectives.
The first technical step is defining an Area of Interest (AOI), which identifies the specific region where analysis will be conducted. A reference grid is then created over this area to serve as the spatial framework for aligning all datasets.
Once the grid is established, the raw data is regridded, standardized, and transformed to fit the reference framework, ensuring spatial consistency across all inputs.
Depending on the selected Module, the data is either:
Transferred from original support to a gridded support.
Enhanced through processes such as smoothing, filtering or data refinement to improve the quality and interpretability of the original information.
Augmented through machine learning models, deep learning feature extraction, or spatial analysis techniques to generate new layers that extend beyond the original datasets.
Feature Processing incorporates a variety of methods, including predictive modelling, regression predictions, computer vision, and stochastic simulations of data for dimensionality reduction, texture analysis, distance calculation, and structural simulation.
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Data Intake: The VRIFY and customer project teams work together to gather project-specific datasets and supplement them with relevant regional public data.
Data Review & Module: Selection VRIFY’s geoscientific team assesses the input data and selects the appropriate Feature Processing Modules based on the project’s objectives and dataset types.
Area Definition & Grid Setup: A reference grid is created over the defined Area of Interest (AOI), forming the spatial foundation for aligning all incoming datasets.
Data Standardization & Transformation (Change of Support): Raw data is regridded and standardized to match the reference grid. This ensures spatial consistency across all inputs through a common support.
Feature Enhancement or Augmentation: Depending on the selected Modules, data is enhanced or augmented. Key methods include predictive modelling, regression predictions, computer vision and stochastic simulations of data.
Output Validation & Expert Review: Outputs are evaluated by VRIFY’s geoscientific team through review of quality control graphs (R²) for interpolated/extrapolated data, visual checks, parameter tracking, and geological reasoning. Project-specific feedback and collaboration with exploration teams ensure that results align with known geology and expectations. Refinements are made as needed.
Delivery of Decision-Ready Grids: The final outputs are spatially aligned, high-quality raster layers designed to reveal patterns, extend the original data, and integrate directly into DORA’s Prediction Maps and other exploration workflows.
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Still have questions?
Reach out to your CSM or email support@VRIFY.com for more information.