What's Inside a GeoMine AI Report (And Why We Built It This Way)

Last month a mine owner from Chitral called me at 11pm. He'd spent 14 lakh rupees on a ground survey that found nothing. His exact words: "Sufyan, I should've just sent you the coordinates first."

He's right. That's the entire reason GeoMine AI exists.

I want to walk you through what actually shows up in one of our reports — not the marketing version, the real one. Because I've seen too many platforms sell vague "AI heatmaps" that look pretty and tell you nothing. If you're paying for a geological intelligence report, you should know exactly what you're getting before the PDF lands in your inbox.

What the report actually contains

Every GeoMine AI report starts with a target area you draw on a map. Could be 5 km². Could be 400 km². Doesn't matter much — the satellite data is already there, sitting in archives going back to 2015 for Sentinel-2 and 1999 for ASTER. We're not flying anything new. We're reading what's already been collected and asking better questions of it.

Here's what shows up inside:

Spectral anomaly mapping. This is the core. We pull Sentinel-2 (10m resolution, 13 bands) and ASTER (15m visible, 30m shortwave infrared, 90m thermal) and run band-ratio analysis specific to whatever mineral you're chasing. Gold prospects? We're looking for iron oxide, clay, and hydroxyl alteration zones — the fingerprints of hydrothermal systems. Copper? Similar story but we weight propylitic and phyllic alteration signatures differently. Lithium brines need a completely different approach using SWIR ratios. Chromite shows up best in band 4/band 8 ratios on Sentinel.

Structural geology layer. This is where SRTM DEM data comes in. 30-meter elevation data, processed for lineaments, fault intersections, and shear zones. Most economic deposits sit on or near structural intersections. If your spectral anomaly lines up with a fault junction, that's a Tier 1 target. If it sits in the middle of nowhere structurally, it's probably noise.

SAR backscatter analysis. Sentinel-1 radar data, mostly useful for surface roughness and moisture content. Honestly, I underused this for the first year. Now I think it's one of the most underrated layers — especially in northern Pakistan where snow cover messes with optical data half the year. SAR doesn't care about clouds.

Confidence scoring per target. Every anomaly gets a score from 1 to 10. We weight spectral signature strength, structural correlation, geological context (using the GSP geological map of Pakistan as a base layer), and historical mining activity within 25km. A score of 8+ means I'd personally send a team. Below 5, I tell clients to deprioritize.

Drilling recommendations. Coordinates. Actual lat/long pairs with priority rankings. Not "this general area looks promising" — specific points with reasoning attached.

Why the workflow matters more than the tech

Look, the satellites aren't ours. Sentinel-2 is European Space Agency. ASTER is NASA-JAXA. SRTM is NASA. Anyone with patience and a Python install can pull this data for free through Google Earth Engine. I've written about that before.

So what are you actually paying for?

You're paying for the interpretation pipeline. The part where raw reflectance values become "there's likely a copper-gold porphyry signature at 35.8421°N, 74.2156°E with an 84% confidence based on alteration mineralogy and proximity to a NE-SW trending fault." That sentence requires roughly 47 processing steps behind it. Atmospheric correction. Topographic normalization. Cloud masking. Band ratio calculation. Threshold tuning per geological province (because what works in Chagai won't work in Gilgit). Cross-referencing with structural data. Filtering false positives from agricultural zones and urban areas.

We've calibrated the geomine AI satellite exploration models against 200+ known deposits across Pakistan, Afghanistan, and parts of Iran where the geology overlaps. That's the moat. Not the data — the calibration.

I got the first version of this wrong, by the way. Early 2023, I was running generic global thresholds and getting too many false positives in Balochistan because the desert varnish was throwing off iron oxide indices. Took us four months to figure out a regional correction. Embarrassing, but that's how these things go.

How to actually read the report when you get it

Three things I tell every new client:

First, don't fall in love with the prettiest red blob. Bright anomalies near roads or villages are often anthropogenic — old slag heaps, construction material, rusted equipment. Check the structural overlay before you get excited.

Second, ground-truth at least two targets before drilling anywhere. Send a geologist with an XRF gun. Spend the 50,000 rupees. Our reports are a filter, not a guarantee — we're narrowing your 400 km² search to maybe 6 specific points so you don't waste two crore on random drilling.

Third, the report is a living document in your head, not ours. Cross-reference it with whatever local knowledge you have. If your grandfather knew there was a copper vein in a specific nullah, and our anomaly map shows nothing there, trust your grandfather and tell us. We've improved the model multiple times based on exactly that kind of feedback.

A standard report covers up to 100 km² and comes back in 5-7 business days. Larger concessions take longer because we manually review every Tier 1 target before it goes out. I still personally check reports for high-value concessions in Gilgit Baltistan — partly because I own 15 mines there and I know the terrain, partly because I don't trust full automation for million-dollar decisions yet.

Maybe I will in two years. Not today.

If you've got a concession sitting idle because you don't know where to start drilling, that's exactly the problem we built geomines for. Send us the boundary KMZ and we'll tell you where the rocks are talking.