Pith Number

pith:44BBWI6P

pith:2026:44BBWI6PAN3CXUSR5TSMJ6IWOT

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Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems

Hui Gao, Jiarun Yang, Xinming Wu, Yimin Dou, Zhixiang Gao

A pretrain-to-alignment paradigm enables accurate geophysical AI despite scarce labels and synthetic-to-field gaps.

arxiv:2605.16783 v1 · 2026-05-16 · physics.geo-ph

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\pithnumber{44BBWI6PAN3CXUSR5TSMJ6IWOT}

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Claims

C1strongest claim

Results from 3,000 field datasets spanning multiple sedimentary basins demonstrate that the proposed paradigm achieves accurate, robust, and well-generalized performance across diverse field surveys, while significantly improving fine-scale stratigraphic and structural details.

C2weakest assumption

That sequential integration of self-supervised pretraining, synthetic supervision, prior-driven refinement, and domain-adaptation fine-tuning will progressively build field-relevant representations and task-specific mapping without introducing inconsistencies or overfitting to the synthetic domain.

C3one line summary

Introduces a staged pretrain-to-alignment workflow for geophysical AI that improves relative geologic time estimation across global field surveys despite limited labels and domain gaps.

References

147 extracted · 147 resolved · 2 Pith anchors

[1] Deep learning for seismic inverse problems: Toward the acceleration of geophysical analysis workflows 2021

[2] Basin analysis: Principles and application to petroleum play assessment 2013

[3] Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review 2023

[4] The quantifiable clinothem--types, shapes and geometric relationships in the plio-pleistocene giant foresets formation, taranaki basin, new zealand 2017

[5] Machine learning in microseismic monitoring 2023

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:21.775305Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e7021b23cf03762bd251ece4c4f91674ecb36a3f0937210b7086ae0be1f3b2e8

Aliases

arxiv: 2605.16783 · arxiv_version: 2605.16783v1 · doi: 10.48550/arxiv.2605.16783 · pith_short_12: 44BBWI6PAN3C · pith_short_16: 44BBWI6PAN3CXUSR · pith_short_8: 44BBWI6P

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/44BBWI6PAN3CXUSR5TSMJ6IWOT \
  | 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: e7021b23cf03762bd251ece4c4f91674ecb36a3f0937210b7086ae0be1f3b2e8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c21f2971de6006b133bf6ab7a3246b10a39ef3180a08e87aa8d02f9165461a81",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.geo-ph",
    "submitted_at": "2026-05-16T03:34:51Z",
    "title_canon_sha256": "b972b04b531983c372809e9e5ed44374986d990b402ba431b664e12f7004f841"
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  "source": {
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    "kind": "arxiv",
    "version": 1
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}