Key Takeaways

  • The raw geophysics (magnetics, gravity, EM survey acquisition) is still classical, mature science — AI enters at the interpretation stage.
  • AI prospectivity-mapping platforms like VRIFY DORA fuse multiple geophysical layers with geochemistry and geology to rank exploration targets automatically.
  • DORA specifically uses a Random Forest model to assign a “prospectivity score” per grid cell after fusing ~24 derived rasters — it’s a real, deployed product, not a research prototype.
  • Competing platforms in this space include GeoVista AI and OreFox, which similarly apply ML to multi-dataset target generation.

TL;DR

Run your standard geophysics acquisition and processing as usual, then feed the processed grids into an AI prospectivity-mapping platform like VRIFY DORA or OreFox, which fuses them with geochemical and geological layers and ranks targets with a trained model instead of a geologist manually overlaying maps.

How Do I Interpret Geophysics With AI?

Geophysics as a discipline — flying a magnetometer survey, running a gravity survey, processing the resulting grids — hasn’t fundamentally changed with AI; that part is still classical geophysical processing (typically in Geosoft Oasis montaj or similar). Where AI has actually changed the workflow is in what happens after you have processed geophysical layers sitting alongside your geochemistry and geological maps: turning that stack of data into a ranked list of drill targets.

VRIFY DORA is the clearest example of this in production. It’s described by its makers as the first AI-assisted mineral discovery platform, and the mechanics are documented: it processes your input datasets into a set of ~24 derived rasters tailored to the deposit type you’re targeting, runs those through a Random Forest model, and assigns each grid cell a prospectivity score, which analysts then refine into priority target zones. It’s a genuinely deployed tool — in one publicized case, an independent DORA-generated target and a human exploration team’s own target both converged on the same gold discovery at Southern Cross’s Sunday Creek project.

OreFox takes a similar data-fusion approach with two linked AI systems — “Prospector AI,” which compares your data against known-deposit signatures, and “Hunter AI,” which refines that comparison into a visual target map. GeoVista AI is a comparable entrant worth evaluating alongside these.

The practical workflow: get your geophysics processed and georeferenced as normal, assemble it with your geochemical and geological layers in a common coordinate system, and upload that stack to one of these platforms rather than manually overlaying and eyeballing anomaly maps yourself.

Try It With Geocluster

Getting geophysics, geochemistry, and geological interpretation into one clean, fusable dataset is most of the real work before any AI targeting tool can help — and it’s exactly the kind of multi-source geoscience data wrangling Geocluster is built for.