Overview
VRIFY's Feature Processing system is a key part of DORAs platform's data preparation pipeline. Its role is to transform, align, and enhance various geoscientific datasets so that they can be used in advanced prediction and mapping tasks, including the generation of DORA Prediction Maps and VRIFY Prospectivity Scores (VPS).
Feature Processing Objectives
The main objectives of feature processing is the change of support and to derive new information and data from existing datasets.
By aligning different types of geological, geochemical, and geophysical data onto a common grid format, Feature Processing creates structured, high-quality inputs for DORA. It also generates new input layers that extend the value of the original data, revealing patterns and relationships critical for exploration analysis. Below is a breakdown of each main objective:
Change of Support
Change of Support is the process of adjusting datasets to a common spatial scale or resolution. In geostatistics, support refers to the physical area or volume over which each data value is averaged or measured. Because different datasets are collected at different scales, standardizing their support is essential for meaningful comparison and analysis.
These differences in scale exist in part because each geoscientific discipline (geochemistry, geology, and geophysics) has developed its own methods, tools, and spatial conventions, for capturing information. Change of support addresses a common challenge in the industry, where data from different specialties is rarely aligned in a way that allows for straightforward comparison.
Change of support is achieved by rasterizing the data, which means converting it into a gridded format where each cell represents a defined spatial unit. Once aligned to a common grid, datasets with different original supports can be combined, integrated, and used reliably in geospatial analysis.
For example, a soil geochemistry sample typically reflects a very local measurement in point support, an airborne geophysical survey provides averaged values over larger areas in block or cell support, and a satellite image delivers regularly spaced observations in a gridded support that may differ in resolution from other inputs. These differing supports must all be transformed into a common, gridded support to be meaningfully processed, compared, and interpreted within the DORA system.
Derive New Information
Once data is standardized through the Change of Support process, it can be used with VRIFY’s proprietary AI Modules for enhancement and augmentation. These Modules use machine learning to generate new insights from existing datasets.
Each Module is specifically optimized for different data types and exploration goals, aligning processing with the structure and characteristics of the input data. Together, they support two key outcomes: improving the clarity and interpretability of known datasets (data enhancement), and generating new modeled information (data augmentation) to extend and enrich exploration efforts.
Data Enhancement focuses on clarifying and refining existing information to improve its usefulness, consistency, and interpretability across datasets. Rather than creating entirely new variables or predictions, this process maximizes the value of raw data by improving its quality and coherence.
Data Augmentation involves generating new data layers through predictive modelling based on known inputs. This approach extends the dataset by identifying patterns and extrapolating them across space, creating valuable new information in previously unmeasured areas.
The result is a more complete and usable dataset, with newly generated layers that extend well beyond the original inputs, giving exploration teams a richer, higher-quality foundation for analysis than they started with.
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Still have questions?
Reach out to your CSM or email support@VRIFY.com for more information.