Pith Number

pith:SJXNXLUX

pith:2026:SJXNXLUXZWNPLUU4UDKPRWC6B4

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DANTE: Physics-Informed Neural Operator for DAS-to-Velocity Waveform Reconstruction Without Co-located Seismometers

Isao Kurosawa

Physics-informed neural operator reconstructs particle velocity from DAS strain rate without seismometers

arxiv:2605.18375 v1 · 2026-05-18 · physics.geo-ph

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On a test set of 200 heterogeneous synthetic wavefields, DANTE achieves a mean output SNR of 15.3 ± 8.8 dB, Pearson correlation r = 0.907, and SSIM = 0.976, corresponding to a mean SNR improvement of approximately +15 dB over the best conventional baseline, and up to +28.8 dB on the most challenging samples. Zero-shot inference on seven real microseismic events yields a kinematic residual of 0.003–0.005.

C2weakest assumption

The assumption that enforcing the exact kinematic relation between DAS strain rate and the spatial gradient of particle velocity together with the one-dimensional elastic wave equation on synthetic heterogeneous media is sufficient to resolve the undetermined integration constant and suppress noise when the model is applied zero-shot to real field data whose heterogeneity and noise statistics may differ from the training distribution.

C3one line summary

A physics-informed Fourier Neural Operator reconstructs particle velocity from DAS strain-rate measurements by enforcing kinematic and elastic-wave-equation constraints, yielding 15.3 dB mean SNR on synthetic tests and low kinematic residuals on real Utah FORGE data without fine-tuning.

References

14 extracted · 14 resolved · 0 Pith anchors

[1] International Conference on Learning Representations , year =

[2] Physics-informed neural networks: 2019

[3] Seismological Research Letters , volume = 2020

[4] Ultrastable laser interferometry for earthquake detection with terrestrial and submarine cables , journal = 2018

[5] Annual Review of Earth and Planetary Sciences , volume = 2021

Receipt and verification

First computed	2026-05-20T00:05:57.840892Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

926edbae97cd9af5d29ca0d4f8d85e0f20d06c7f50d304a281ab43fc49d0ccc1

Aliases

arxiv: 2605.18375 · arxiv_version: 2605.18375v1 · doi: 10.48550/arxiv.2605.18375 · pith_short_12: SJXNXLUXZWNP · pith_short_16: SJXNXLUXZWNPLUU4 · pith_short_8: SJXNXLUX

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SJXNXLUXZWNPLUU4UDKPRWC6B4 \
  | 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: 926edbae97cd9af5d29ca0d4f8d85e0f20d06c7f50d304a281ab43fc49d0ccc1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fc48121421a6ac7ce25761e39de512efe4c8f87bd4eacec4145348d538250ca4",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.geo-ph",
    "submitted_at": "2026-05-18T13:22:12Z",
    "title_canon_sha256": "cd8d0dca9e3016a98c13bba248004b50686cc277e7011e54f63c3ad2733046a2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.18375",
    "kind": "arxiv",
    "version": 1
  }
}