pith:OZAXFIOK
Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
Mapping relative geologic time to a sinusoidal space lets deep learning produce more accurate and topologically consistent stratigraphic fields from 3D seismic data.
arxiv:2605.01273 v3 · 2026-05-02 · physics.geo-ph
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\pithnumber{OZAXFIOKJ23ZIZQAMONHDV5UYG}
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Record completeness
Claims
RGT-Est achieves state-of-the-art performance among AI-based methods without horizon constraints, and attains substantially higher horizon-correlation accuracy and global topological consistency once sparse priors are incorporated.
That transforming RGT into a sinusoidal space explicitly encodes periodic stratigraphic semantics and alleviates over-smoothing of fine horizons without introducing new artifacts or losing topological information in complex real-world structures.
RGT-Est transforms RGT estimation into a sinusoidal space with joint losses to capture fine horizons and global stratigraphic order from seismic data, outperforming prior AI methods especially with sparse horizon priors.
Formal links
Receipt and verification
| First computed | 2026-05-21T01:05:19.705349Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
764172a1ca4eb7946600639a71d7b4c18193433ba7fc2237174707876ea2a9db
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OZAXFIOKJ23ZIZQAMONHDV5UYG \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 764172a1ca4eb7946600639a71d7b4c18193433ba7fc2237174707876ea2a9db
Canonical record JSON
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"primary_cat": "physics.geo-ph",
"submitted_at": "2026-05-02T06:18:45Z",
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