Geophysical Data Preparation for AI-Assisted Mineral Discovery with DORA

VRIFY’s Geophysics Team supports optimization of exploration datasets, enabling our clients to focus on using DORA to generate defensible targets.

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Technical Deatils

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Exploration data reflects the circumstances of its collection. Some surveys are captured with the precision of modern instruments, while others bear the limitations of older methods or challenging field conditions. The result is information that remains valuable but often incomplete and inconsistent when combined in a single project.

This variability is the environment in which DORA is applied, and where it stands out. The software turns diverse datasets into exploration targets quickly and at scale, grounding each prediction in geological context with clear reasoning and confidence levels. Because the quality of these outputs depends largely on that of the inputs, disciplined data preparation becomes one of the first steps in effectively using DORA.

For many of our partners, this stage can be particularly demanding. The work is detailed and often falls outside the mandate of external consultants. At VRIFY, a dedicated team of geophysicists supports this stage, ensuring clients’ datasets are ready for the software so that the focus remains on target generation.

Data Standardization and Transformation

This effort extends beyond file compilation and conversion, into steps that ensure comparability across the same ground. Currently, as DORA ingests data in 2D, geophysical inputs are first transformed into rasters. Point and line measurements are interpolated into grids that cover the area of interest, while sections from inversion models are extracted at different elevations, capturing changes in geological domains and contacts with depth. Where methods of similar resolution overlap, in-house scripts merge and level data to produce seamless layers, maximizing continuous information coverage. Interpreted products are carried further: Electromagnetic picks and lineaments can be converted into distance-to-conductors layers and, where conditions support it, developed into strike-field maps that can highlight structural patterns.

Data Augmentation

Once datasets have been standardized and transformed, attention shifts to signal enhancement. Potential field data, such as magnetics and gravity, are processed using a suite of Fourier filters, which are mathematical tools that amplify or attenuate certain frequencies and highlight edges and boundaries (Figure 1). They help remove noise where present, while revealing subtle signals and sharpening the expression of structural fabric that might otherwise remain muted. Additional refinement is achieved through machine-learning routines, including inpainting (Figure 2), which is a gap-filling script that restores coverage where data is missing due to cultural noise. Finally, lineament mapping is conducted (Figure 3), which adds potential geological context detail in areas with weaker signal.

Figure 1. An example of Fourier filters applied to public regional magnetic data from Saskatchewan, Canada via the Saskatchewan Geological Survey. (A) Data reduced to the pole (RTP) serves as the baseline. (B) Tilt angle emphasizes anomaly tops and edges and enhances subtler features. (C) First-vertical-derivative (1VD) filter sharpens anomalies and enhances shallow sources. (D) Horizontal derivative–north–south (HD–NS) filter enhances features trending perpendicular to the north–south direction.
Figure 2. An example of inpainting applied to public magnetic data from Quebec, Canada via the SIGÉOM (Système d’information géominière). (A) The original image shows how gaps can occur in the data as a result of masking artifacts created by cultural noise. (B) The inpainted image shows that the script reconstructs the magnetic grid to provide continuous data for analysis.
Figure 3. An example of lineament mapping applied to public magnetic data from Geoscience BC’s QUEST project in central British Columbia, Canada. (A) HD-NS filter-processed magnetic map, with lineament density (B) and complexity (C) maps generated for its southern portion (indicated by rectangle). (B) Lineaments highlight contacts between rocks of varying magnetic response that are related to lithological contacts such as that between volcanic rocks of different compositions, country rocks and intrusions, and/or juxtaposition of rocks caused by faulting. Faults and lithological contacts are important in the localization of mineralization in many mineral systems, including porphyry systems (Seedorf et al., 2005) and orogenic gold systems (Goldfarb et al., 2005). (C) Lineament complexity map, shown as a heat map of the proximity of lineaments and their intersections that are potential traps and conduits for mineralizing systems.

Not all geophysical survey files arrive in a standard form, and in these cases our team develops tailored solutions to make them usable. For example, on one project, magnetic data available only as RGB imagery was converted into a workable grid through a script designed to approximate values from pixels. Public government surveys are also integrated as needed, adding regional perspective that complements proprietary information and helps connect shallow features seen in local rasters with deeper, larger-scale structures.

In addition to supporting data preparation for DORA, our Geophysics Team highlights gaps that may remain. By making these absences clear, we help our partners plan exploration campaigns that address missing data and improve future predictions.

Through this careful preparation, we empower exploration teams with datasets that are more complete and coherent for analysis, giving DORA a stronger base to generate defensible targets. If you have questions about preparing data for DORA or the overall onboarding process, book a demo with our team.

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References:

Goldfarb, R.J., Baker, T., Dubé, B., Groves, D.I., Hart, C.J.R., & Gosslein, P. (2005). Distribution, character, and genesis of gold deposits in metamorphic terran. Economic Geology 100th Anniversary Volume, p. 407–450, Society of Economic Geologists.

Seedorff, E., Dilles, J.H., Proffett, J.M., Einaudi, M.T., Zurcher, L., Stavast, W.J.A., Johnson, D.A., & Barton, M.D. (2005). Porphyry deposits: Characteristics and origin of hypogene features. Economic Geology 100th Anniversary Volume, p. 251–298, Society of Economic Geologists.

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