Methodology, front-to-back.
Everything a court, a regulator, an academic reviewer, or an insurance underwriter needs to evaluate the DeepMap AI earthquake magnitude cascade — its training lineage, its calibration references, its measured error rate, and the public audit trail for every claim on this site.
Reproducibility: every number on this page traces back to a file in the open source tree or a public upstream dataset. Source: github.com/Mljessop/SensorAnalytics.
The PDF carries the same content and the same public URL footer, so legal teams can file it as-is.
1. The five Daubert prongs, addressed directly.
Daubert v. Merrell Dow (509 U.S. 579, 1993) set five non-exclusive factors for admitting expert scientific testimony. We address each explicitly because any industrial customer of the cascade forecast will eventually need this for an adjuster, a regulator, or a court.
Every prediction is logged to a SHA-256 hash-chained ledger before the event window opens. The file common/prediction_ledger.py writes each prediction with its magnitude band, validity horizon, and confidence. When an observation lands, the match logic is deterministic (bounds-first + magnitude-scaled radius) and documented in the same file. Anyone can replay the match against any USGS or EMSC event of their choosing.
The calibration references are peer-reviewed (Wells & Coppersmith 1994, Reasenberg & Jones 1989, Gardner & Kirby 2011 for isotope validation). The cascade model itself is a research preview — we are publishing ahead of peer review deliberately, and we invite researchers to audit the hash chain and contribute to a joint methodology paper. No commercial claim is made that depends on the cascade model being already peer-reviewed.
Published openly and updated weekly on /magnitude-cascade. Current operational numbers (April 2026):
- Phase 6 magnitude regressor: MAE 0.41 on subduction, MAE 0.42 on intraplate, classifier accuracy 88.8–93.1% depending on tectonic regime.
- Bounds-first earthquake matcher: 20.1% any-match, 6.5% full-credit on the Phase 65e honest re-scoring (940 predictions, 6-week sample).
- Tensor-trained EQ classifier (outcome-labeled): LOOCV AUC 0.917, Brier 0.124 (n=32 initial, will grow with */15 min snapshots).
- Tensor-trained solar flare classifier: LOOCV AUC 0.995, Brier 0.017 (n=70).
Geophysical calculations follow published standards. Gravity reductions use GRS80 (Moritz 1980) with Bouguer slab correction. Seismic data access is federated FDSN per IRIS standards. Solar input uses NOAA SWPC GOES X-ray flux and ACE/DSCOVR solar wind — public, versioned feeds. Magnitude scale is moment magnitude (Mw) as harmonized by the USGS unified catalog where available, ML fallback when not.
The individual components are broadly accepted: seismic ambient-noise interferometry (dv/v stretching, e.g. Brenguier et al. 2008), Schumann resonance anomaly monitoring (Rakov & Uman 2003), GRACE-FO gravity, IGETS superconducting gravimetry, and coincidence-based multi-physics analysis. The composition of these into a single Earth State Tensor and a per-region cascade is novel to DeepMap AI — the research preview posture exists precisely because novel composition has not yet earned general acceptance.
2. What the cascade does, in one page.
For every one of the ~54 monitored earthquake regions, each pipeline run asks the Phase 6 regressor: “what magnitude do you expect for this region, and with what confidence?” The output is mapped to an M0.5 band (M3.0 felt-floor through a region-specific tectonic ceiling). If confidence is at or above each threshold, the ledger logs a tiered cascade:
Every tier carries the M0.5 band (M0.25 once a region earns it — see § 3), a 95% CI from the regional Phase 6 sigma, a SHA-256 hash linking to the previous ledger entry, and a cascade_id grouping the tiers for that region and hour.
3. Per-region band graduation (M0.5 → M0.25).
A region begins at the default M0.5 band width. Graduation to M0.25 requires, for that specific region:
- ≥ 30 matched observations against past cascade predictions.
- Robust residual σ (median absolute deviation × 1.4826) of ≤ 0.30 magnitude units between predicted point estimate and observed magnitude.
No region is narrowed by fiat. The predictor consults the ledger at every logging call, computes the region’s residual sigma on the fly, and only emits an M0.25 band when the criteria are met. Reference implementation: cross_physics/magnitude_shadow_predictor.py ⇒ _adaptive_band_width().
4. What we do not claim.
- We do not claim deterministic earthquake prediction. No one can, and anyone who does is selling. The cascade is a probabilistic forecast.
- We do not claim single-event attribution. A post-event narrative “we predicted that one” is only valid when the prediction is in the hash chain with an earlier timestamp than the event.
- We do not claim the tensor-trained AUC numbers generalize out of distribution. LOOCV on a small sample is a first result, not a settled operational metric.
- We do not claim SLA-backed reliability. This is a research preview; the commercial tier (when it opens) will carry the SLA, indemnity, and on-call coverage explicitly.
5. How to audit any specific prediction.
- Find the prediction on /ledger-verify or /api/v1/ledger/recent.
- Confirm its hash chains backwards to a known-good entry (timestamp anchors in the timestamps/ directory are OpenTimestamps-anchored to Bitcoin).
- Query USGS / EMSC for events in its bounding box and magnitude band during its validity window.
- Re-run the bounds-first matcher locally from common/prediction_ledger.py to confirm the match or miss.
Disclosure version: v2026.04.19. Last reviewed by author: Michael Jessop, DeepMap AI. For questions or formal review requests, contact michael@deepmapai.com.