Paper Citation Record · LEDGER

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization?

As of 21 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 1 inbound Pith citation observation for arXiv:2506.05484.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.05484 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:26:40.317479Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T01:58:02.062722Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T01:58:28.960812Z

Reference resolution

9 of 9 outbound references displayed

verified exact0
verified fuzzy7
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d39a6aa9-4ed0-4041-a667-802367271981 · outbound

This paper cites Implicit seismic full wave- form inversion with deep neural representation,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Implicit seismic full wave- form inversion with deep neural representation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:41.939717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bab5603e-246a-4000-a166-6b1bcf975db1 · outbound

This paper cites An overview of full-waveform inversion in exploration geophysics,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? An overview of full-waveform inversion in exploration geophysics,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:39.772377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb1f168-96e7-48d0-ac2b-1be27ed36e1d · outbound

This paper cites Review of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Review of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:41.662833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 81f6e1ae-9e20-45fa-bd4e-9863a1dc3d9d · outbound

This paper cites Physics-guided data-driven seismic inversion: Recent progress and future opportunities in full-waveform inversion,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Physics-guided data-driven seismic inversion: Recent progress and future opportunities in full-waveform inversion,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:41.438010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0b5df9cd-fb77-4656-af63-3070d76002ef · outbound

This paper cites Parametric convolutional neural network- domain full-waveform inversion,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Parametric convolutional neural network- domain full-waveform inversion,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:41.224228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a7bec68-f745-485c-ac20-b53236276775 · outbound

This paper cites Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:41.055120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 936e2f14-943d-44f9-9d76-095b0410efaa · outbound

This paper cites Overview frequency princi- ple/spectral bias in deep learning,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Overview frequency princi- ple/spectral bias in deep learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:40.841037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0422ae9b-a58e-4297-9565-14894e77e559 · outbound

This paper cites A survey on negative transfer,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? A survey on negative transfer,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:26:40.596600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4467f511-36d2-4e82-8430-ae2787c90055 · outbound

This paper cites Loss of plasticity in deep continual learning,.

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization? Loss of plasticity in deep continual learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:26:40.317479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d381002c-0665-4764-b0f5-4fb4370cfe36 · inbound

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel cites this paper.

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization?

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:58:28.962372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.