Experimental and future technologies: Chapter A — Seafloor exploration · Chapter B — Minerals and the subsurface
Research Archive & Validation History
GeaSpirit fuses computational materials science with satellite remote sensing to detect mineral prospectivity from public-access data. This page is the engine's working logbook — schematic and honest: what each run measured, what failed, where the real ceiling is, and the road to a sellable exploration report and, ultimately, a mining-asset marketplace.
Operating philosophy. Make → Measure → Fix → Repeat. Every sprint must produce something demonstrable. Stack: 100% public-access data and open tools (Sentinel-2/1, Landsat, SRTM, EMIT, USGS / national geological surveys, Macrostrat, Google Earth Engine). No invented figures, ever.
1. The pipeline
For any area of interest the engine runs a fixed sequence and outputs a ranked target list with confidence:
- Acquire — pull public-access satellite, terrain, geophysics and geology layers over the AOI.
- Feature build — spectral mineral indices, thermal climatology, terrain/structure, geology, vegetation.
- Type-aware fusion — select the optimal feature subset for that deposit type and zone.
- Score & calibrate — a calibrated ranking surface under spatial cross-validation. It orders ground by model score; it is not a probability that mineralisation is present.
- Rank & report — ranked targets with exact coordinates, evidence breakdown and limitations.
2. Sprint plan (0 → sellable report)
| Sprint | Goal | Key metric |
|---|---|---|
| 0 | Setup & public data acquisition | Load a Sentinel-2 scene |
| 1 | First mineral-index maps | Visual correlation with known deposits |
| 2 | First ML classifier | AUC > 0.65 |
| 3 | Multi-sensor fusion | AUC > 0.72 |
| 4 | Vegetation as a sensor | +2% AUC from vegetation |
| 5 | Transfer learning | AUC > 0.60 on a new zone |
| 6 | Deposit-model integration | 3/5 known deposits found |
| 7 | First sellable report | Positive geologist feedback |
3. Recorded results — multi-zone, multi-deposit, multi-continent
Conservative, leakage-resistant spatial cross-validation AUC. Higher is better (0.5 = random).
| Zone | Deposit type | Baseline | Full fusion |
|---|---|---|---|
| Chuquicamata (Chile) | Porphyry Cu | 0.79 | 0.88 |
| Kalgoorlie (Australia) | Orogenic Au | 0.81 | 0.94 |
| Zambia Copperbelt | Sediment-hosted Cu | 0.74 | 0.76 |
| Peru | Porphyry Cu | 0.70 | 0.76 |
| Arizona (USA) | Porphyry Cu | 0.72 | 0.72 |
Where these figures come from. They are the best recorded configuration per zone, and the configurations differ between zones. The cross-domain comparison run — same 12 bands, no EMIT, no geophysics — records Chuquicamata 0.862, Kalgoorlie 0.769 and Zambia 0.615. An AUC is a ranking score inside one experiment: it is not a calibrated probability of mineralisation, and it carries no depth and no tonnage.
Fusion is not universal: it lifts strong-baseline zones and can hurt weak ones. The engine therefore selects features per zone and per deposit type — there is no single planet-wide formula.
4. Feature families — what each one contributed
| Family (public-access source) | Verdict |
|---|---|
| Satellite spectral baseline (Sentinel-2 / Landsat) | Core — always included |
| 20-year thermal climatology (Landsat) | Contributing input feature in these experiments — universal, modest lift. Not a validated detection method; the standalone thermal hypothesis is PARKED. |
| EMIT hyperspectral alteration (NASA) | Contributing input feature — porphyry Cu experiments only. The standalone spectral-residual hypothesis was NOT RUN. |
| Geology / lithology (Macrostrat) | Contributing input feature — selective, on three-zone evidence. |
| Aeromagnetics (national surveys) | Selective — small but real lift |
| Multi-decadal NDVI / vegetation (Landsat) | Selective — vegetated zones |
| Terrain / spatial gradients | Redundant with spectral — not added |
5. Key learnings
- Labels dominate. Same satellite data, Kalgoorlie went from 0.58 → 0.77 just by going from 16 to 205 curated deposit labels.
- Deposit type > commodity > geography. Training on pure types sharpens the model; mixing types confuses it.
- Fusion needs a floor. A zone needs a baseline AUC ≈ 0.73 before extra layers help rather than hurt.
- Transfer is zone-specific. Satellite features encode geography, not transferable geology — each zone earns its own stack.
- The gap is data, not ML. Surface screening is near its ceiling; the next gains come from new public-access layers.
6. Honest scorecard
We grade the engine against a fixed four-dimension objective. This is the real number, not a marketing one:
| Dimension | Score | Status |
|---|---|---|
| Mineral identity | 4.0 / 10 | Satellite encodes geography, not mineralogy |
| Depth | 4.1 / 10 | Needs subsurface geophysics (gravity / AEM) — access-blocked |
| Coordinates | 7.0 / 10 | ~30 m resolution, ~1 km² zones |
| Certainty | 7.7 / 10 | Calibrated Brier 0.1955 → 0.1711 (isotonic, n=211,592). Lower per-fold values exist in individual ablations; the calibrated model is the figure that is quoted. |
| Total | 22.8 / 40 (57%) | Surface screening solid; depth is the frontier |
Path to the top. Satellite only caps near 55%. Adding public-access geophysics lifts it toward 75%; adding drill-hole data toward 90%. A true 100% needs a field campaign — which is exactly what a GeaSpirit report is designed to de-risk and prioritize before a single dollar is spent in the field.
7. Limitations
- Remote sensing and public data screen opportunity; only drilling confirms a deposit.
- Scores are prioritization indicators — not probabilities of economic mineralisation, and not valuations.
- Depth estimation is limited by access to subsurface geophysics, much of which sits behind manual portals.
- GeaSpirit provides intelligence and prioritization, never investment, legal or technical advice.
8. Where this is going
- Screening reports — where a zone ranks, on what evidence, with what is missing. A ranking is not a statement that a mineral is present.
- Global coverage — scale the public-access database toward every mine and prospect on Earth.
- Mining-asset marketplace — connect owners, explorers and investors (individuals & institutions) around evidence-scored assets and potential lands, with verifiable, timestamped provenance.