How DeepMap AI actually works.
No black boxes. This page walks through the full pipeline — from raw geophysical data ingestion, through the 16,000+-dimensional Earth State Tensor, to the classifiers that fire live alerts and the ledger that pins every prediction to a tamper-evident hash chain.
The pipeline, end to end
Five stages, each built from public data + peer-reviewed physics.
What the accuracy numbers mean
AUC translated for humans.
You'll see AUC 0.92 (cross-validated) on the tensor earthquake classifier and AUC 0.999 on the solar flare classifier. That's Area Under the ROC Curve — a standard machine-learning metric. It is not an accuracy figure, and we don't write it as one.
Unpacked: if we pick a random time when a significant event happened and a random time when it didn't, the model ranks the event-time higher 92% of the time for earthquakes and 99.9% for solar flares. Both figures come from cross-validation, and both carry caveats we set out below. The solar figure in particular should be treated as unvalidated: every positive label in its training window falls inside a single 7.8-hour flare episode, so a forward-in-time test of it is mathematically undefined rather than merely uncertain. The live-ledger hit rate is the honest number for that product.
The validation method is LOOCV — Leave-One-Out Cross-Validation, and in July 2026 we established that it flatters this particular model. We re-tested the per-region earthquake classifier with a temporal holdout — train only on earlier data, score only on later data — and the mean AUC fell from 0.992 to 0.493. That is chance. The cause is that our tensor snapshots are 15 minutes apart while carrying a 7-day forward label, so a randomly held-out sample has near-identical neighbours sitting in the training set with the same label. Random folds cannot separate them; a temporal split can. We are publishing this because it is exactly the kind of thing a reader should not have to discover for us.
Important caveat we don't hide: AUC is an ordering metric, not a pointwise hit rate. High AUC means the model is excellent at ranking — not that every alert will fire correctly. We pair AUC with operating-point tables (precision at threshold, recall at threshold) to pick the right precision/recall trade-off for each product. High-confidence alerts get tight thresholds (fewer, more certain); broad-coverage products get looser ones (more, less certain).
Rule of thumb
| 0.50 | Coin flip |
| 0.70 | "It's doing something" |
| 0.80 | Operationally useful |
| 0.90 | Strong — publishable |
| 0.917 | Tensor EQ (ours) |
| 0.95 | Excellent |
| 0.999* | Tensor Solar — *LOOCV, n≈70; we report the live-ledger hit rate as the operational number |
The flywheel
Why accuracy compounds every week.
Most ML systems ship once and decay. DeepMap AI is built as a flywheel — every confirmed outcome becomes a new labeled training sample, and the whole stack retrains on it.
Verification
Every claim on this site, reproducible.
Three ways to independently verify any number we publish:
sha256_hash. The chain itself is queryable at /api/v1/ledger/integrity. Any row rewrite would break every subsequent hash. Try it →
Scientific advisory panel
The people who keep us honest.
Independent domain experts who review our methods, challenge our claims, and hold the platform to discipline-standard rigor. Active recruitment of three founding advisors: seismology, isotope hydrology, and reinsurance quantitative risk.
Advisory compensation: modest equity grant (DeepMap AI Inc., Wyoming). Typical vest: 18-24 months with a 6-month cliff. No publication restrictions; we encourage advisors to publish their own reviews.
For scientists & researchers
Bring your own event, test the pipeline.
Create a free account to get programmatic access to the ledger, the tensor eigenmode stream, and the convergence-gate feed. Query historical predictions by region, by confidence, by hazard type. Reproduce our backtests against your own event list.
Free researcher account API docs