Paper Citation Record · LEDGER

A generative foundation model for an all-in-one seismic processing framework

As of 15 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 1 inbound Pith citation observation for arXiv:2502.01111.

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

pith.paper-citation-record.v1
2502.01111 v1

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Reference resolution

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External citation measurements

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Outbound references

Observation da90a15f-5c83-4ce4-a60f-806184e17b45 · outbound

This paper cites Seismic data analysis: Processing, inversion, and interpretation of seismic data.

A generative foundation model for an all-in-one seismic processing framework Seismic data analysis: Processing, inversion, and interpretation of seismic data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.911310Z

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Observation 0ef78f86-7a68-4223-9bde-c6a9970e540e · outbound

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

A generative foundation model for an all-in-one seismic processing framework An overview of full-waveform inversion in exploration geophysics

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.902804Z

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Observation d02d2eb6-2aa6-4a6b-8758-3fd08c7fcf66 · outbound

This paper cites Lateral prediction for noise attenuation by tx and fx techniques.

A generative foundation model for an all-in-one seismic processing framework Lateral prediction for noise attenuation by tx and fx techniques

Reference 3

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Observation 6eba22a7-e60d-4abf-b359-826dc251f181 · outbound

This paper cites Introduction to this special section—seismic noise.

A generative foundation model for an all-in-one seismic processing framework Introduction to this special section—seismic noise

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.885440Z

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Observation 4d9c3a05-5958-4252-9d66-3bf70afbc11b · outbound

This paper cites Random noise attenuation by fx empirical-mode decomposition predictive filtering.

A generative foundation model for an all-in-one seismic processing framework Random noise attenuation by fx empirical-mode decomposition predictive filtering

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.875804Z

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Observation dddfaa49-3e7a-4851-a9e9-519f8c2d710a · outbound

This paper cites Random noise attenuation using local signal-and-noise orthogonalization.

A generative foundation model for an all-in-one seismic processing framework Random noise attenuation using local signal-and-noise orthogonalization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.866102Z

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Observation 50396f23-8f3d-4b29-9e74-3c78f6edfd46 · outbound

This paper cites Signal and noise separation in prestack seismic data using velocity-dependent seislet transform.

A generative foundation model for an all-in-one seismic processing framework Signal and noise separation in prestack seismic data using velocity-dependent seislet transform

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.856220Z

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Observation be095bd7-5f1d-4322-90ab-2db59fe2ed05 · outbound

This paper cites Static corrections for seismic reflection surveys.

A generative foundation model for an all-in-one seismic processing framework Static corrections for seismic reflection surveys

Reference 8

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Observation efa5dd6c-dfb3-48a2-8779-4b3c8519c1fb · outbound

This paper cites Adaptive surface-related multiple elimination.

A generative foundation model for an all-in-one seismic processing framework Adaptive surface-related multiple elimination

Reference 9

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Observation 63d1b484-5a9d-4ca6-9422-7343600644a8 · outbound

This paper cites Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction.

A generative foundation model for an all-in-one seismic processing framework Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.827434Z

Source-reported events for the cited work

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Observation def327a8-fa4a-42ea-9919-97550291769c · outbound

This paper cites Seismic trace interpolation in the fx domain.

A generative foundation model for an all-in-one seismic processing framework Seismic trace interpolation in the fx domain

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.817872Z

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Observation c669cf37-7f9b-43a0-be15-15a338383a4b · outbound

This paper cites Seismic trace interpolation in the fxy domain.

A generative foundation model for an all-in-one seismic processing framework Seismic trace interpolation in the fxy domain

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.808972Z

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Observation a75c2000-479a-465b-a092-574393807f5b · outbound

This paper cites The interpolation of sparse geophysical data.

A generative foundation model for an all-in-one seismic processing framework The interpolation of sparse geophysical data

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.800173Z

Source-reported events for the cited work

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Observation 0eb088b2-1178-4b62-ad65-d47a95da9cb5 · outbound

This paper cites Velocity analysis for transversely isotropic media.

A generative foundation model for an all-in-one seismic processing framework Velocity analysis for transversely isotropic media

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.791297Z

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Observation ca6a4fdd-2ce0-4ee9-8ba2-b9705e228212 · outbound

This paper cites Migration velocity analysis and waveform inversion.

A generative foundation model for an all-in-one seismic processing framework Migration velocity analysis and waveform inversion

Reference 15

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Observation a4f1a045-6186-4aeb-b427-d0d26f9d008e · outbound

This paper cites Velocity analysis using ab semblance.

A generative foundation model for an all-in-one seismic processing framework Velocity analysis using ab semblance

Reference 16

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Source-reported events for the cited work

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Observation c4720b35-179a-4f50-9dd1-aed5c257647c · outbound

This paper cites Reverse time migration.

A generative foundation model for an all-in-one seismic processing framework Reverse time migration

Reference 17

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Source-reported events for the cited work

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Observation ea219306-34c4-4d61-bd94-e3a8ab261745 · outbound

This paper cites Elastic reverse-time migration.

A generative foundation model for an all-in-one seismic processing framework Elastic reverse-time migration

Reference 18

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Source-reported events for the cited work

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Observation d881ed29-a623-4b10-89d0-75cd4e1fad16 · outbound

This paper cites A stable and practical implementation of least-squares reverse time migration.

A generative foundation model for an all-in-one seismic processing framework A stable and practical implementation of least-squares reverse time migration

Reference 19

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Observation 6a2b92fd-8758-4f5c-94f1-1f196969644d · outbound

This paper cites An overview of depth imaging in exploration geophysics.

A generative foundation model for an all-in-one seismic processing framework An overview of depth imaging in exploration geophysics

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.735792Z

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Observation a2675ca1-cd40-4c66-a945-835055043043 · outbound

This paper cites Inversion of seismic reflection data in the acoustic approximation.

A generative foundation model for an all-in-one seismic processing framework Inversion of seismic reflection data in the acoustic approximation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.725153Z

Source-reported events for the cited work

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Observation 915b2c61-df6e-445d-badf-18aca0b3806a · outbound

This paper cites A strategy for nonlinear elastic inversion of seismic reflection data.

A generative foundation model for an all-in-one seismic processing framework A strategy for nonlinear elastic inversion of seismic reflection data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.714340Z

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Observation e1b0867d-b915-4ce7-8cd1-fd6174d2554c · outbound

This paper cites From tomography to full-waveform inversion with a single objective function.

A generative foundation model for an all-in-one seismic processing framework From tomography to full-waveform inversion with a single objective function

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.704056Z

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Observation 23d17ea9-5fc0-4379-b44b-9356cd20f4d0 · outbound

This paper cites Deep learning for geophysics: Current and future trends.

A generative foundation model for an all-in-one seismic processing framework Deep learning for geophysics: Current and future trends

Reference 24

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Observation a0d387b7-f3ea-4338-a83f-622c724958e1 · outbound

This paper cites Machine learning for seismic processing: The path to fulfilling promises.

A generative foundation model for an all-in-one seismic processing framework Machine learning for seismic processing: The path to fulfilling promises

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.683706Z

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Observation 95ea9d3c-18f7-4318-820e-a08c7a11c9c0 · outbound

This paper cites Deep-learning inversion of seismic data.

A generative foundation model for an all-in-one seismic processing framework Deep-learning inversion of seismic data

Reference 26

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Observation 83c145c5-d20c-4183-9422-8dd53bfb91fd · outbound

This paper cites Deep-learning seismology.

A generative foundation model for an all-in-one seismic processing framework Deep-learning seismology

Reference 27

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Observation 49568204-72f5-4509-919c-cd2d4c912db8 · outbound

This paper cites Applications of deep neural networks in exploration seismology: A technical survey.

A generative foundation model for an all-in-one seismic processing framework Applications of deep neural networks in exploration seismology: A technical survey

Reference 28

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Observation b3c3d3ac-4a2e-493e-a5a4-988eaa5a0b9d · outbound

This paper cites Deep learning for denoising.

A generative foundation model for an all-in-one seismic processing framework Deep learning for denoising

Reference 29

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Observation 493533bb-70cf-4775-8cb5-52c494927869 · outbound

This paper cites Deep-learning-based seismic data interpolation: A preliminary result.

A generative foundation model for an all-in-one seismic processing framework Deep-learning-based seismic data interpolation: A preliminary result

Reference 30

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Observation 09c145de-764a-4f9c-ac4a-d03ab3a9f901 · outbound

This paper cites Desert low-frequency noise suppression by using adaptive dncnns based on the determination of high-order statistic.

A generative foundation model for an all-in-one seismic processing framework Desert low-frequency noise suppression by using adaptive dncnns based on the determination of high-order statistic

Reference 31

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Observation bb6ce96c-71b7-4034-8ca5-52427d39dc10 · outbound

This paper cites Faultseg3d: Using synthetic data sets to train an end-to-end convolutional neural network for 3d seismic fault segmentation.

A generative foundation model for an all-in-one seismic processing framework Faultseg3d: Using synthetic data sets to train an end-to-end convolutional neural network for 3d seismic fault segmentation

Reference 32

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Observation d7c7cb01-5208-428d-bd37-6caf3733e565 · outbound

This paper cites Building realistic structure models to train convolutional neural networks for seismic structural interpretation.

A generative foundation model for an all-in-one seismic processing framework Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Reference 33

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Observation e5b5736d-1868-4a6d-b9fb-c0091fe16260 · outbound

This paper cites Deep-learning full-waveform inversion using seismic migration images.

A generative foundation model for an all-in-one seismic processing framework Deep-learning full-waveform inversion using seismic migration images

Reference 34

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Observation f6a0a78b-d9e2-408a-aabf-9fc17c588059 · outbound

This paper cites Can deep learning compensate for sparse shots in the imaging domain? a potential alternative for reducing the acquisition cost of seismic data.

A generative foundation model for an all-in-one seismic processing framework Can deep learning compensate for sparse shots in the imaging domain? a potential alternative for reducing the acquisition cost of seismic data

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.601627Z

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Observation 4dd611a0-554e-40e3-b2d5-5d54aa30432d · outbound

This paper cites Seismic data reconstruction based on a multicascade self-guided network.

A generative foundation model for an all-in-one seismic processing framework Seismic data reconstruction based on a multicascade self-guided network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.591449Z

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Observation c7758064-8c60-429f-81b1-d1be1e0bb529 · outbound

This paper cites Mlreal: Bridging the gap between training on synthetic data and real data applications in machine learning.

A generative foundation model for an all-in-one seismic processing framework Mlreal: Bridging the gap between training on synthetic data and real data applications in machine learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.581624Z

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Observation 4f305ff6-ce50-40c7-8d29-3ccb8011183d · outbound

This paper cites Improving the generalization of deep neural networks in seismic resolution enhancement.

A generative foundation model for an all-in-one seismic processing framework Improving the generalization of deep neural networks in seismic resolution enhancement

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.572157Z

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Observation ed77e579-a3a6-4e0d-a667-a47d52db3ec4 · outbound

This paper cites Deep denoising autoencoder for seismic random noise attenuation.

A generative foundation model for an all-in-one seismic processing framework Deep denoising autoencoder for seismic random noise attenuation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.562537Z

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Observation 7a94e1c6-1e80-406d-a04a-11c1de5d32d3 · outbound

This paper cites The potential of self-supervised networks for random noise suppression in seismic data.

A generative foundation model for an all-in-one seismic processing framework The potential of self-supervised networks for random noise suppression in seismic data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.552306Z

Source-reported events for the cited work

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Observation 38c6f147-799a-4d08-ae33-063bf5eef0ad · outbound

This paper cites Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme.

A generative foundation model for an all-in-one seismic processing framework Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.542578Z

Source-reported events for the cited work

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Observation ac028965-1a1a-47e6-bf46-657af5b70be4 · outbound

This paper cites A self-supervised scheme for ground roll suppression.

A generative foundation model for an all-in-one seismic processing framework A self-supervised scheme for ground roll suppression

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.533016Z

Source-reported events for the cited work

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Observation d6965f9f-5d37-486a-bf69-1d13b78070d8 · outbound

This paper cites Gabor-based learnable sparse representation for self-supervised denoising.

A generative foundation model for an all-in-one seismic processing framework Gabor-based learnable sparse representation for self-supervised denoising

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.524355Z

Source-reported events for the cited work

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Observation 4415b431-b2cf-4aa1-bab4-85e6ba98aa0e · outbound

This paper cites Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network.

A generative foundation model for an all-in-one seismic processing framework Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.515446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 46936b70-f14f-4c33-a880-d0010eb579d4 · outbound

This paper cites An effective self-supervised learning method for attenuating various types of seismic noise.

A generative foundation model for an all-in-one seismic processing framework An effective self-supervised learning method for attenuating various types of seismic noise

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.506784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c756730-f1c6-436d-81a0-f36bffb380ca · outbound

This paper cites A self-supervised learning framework for seismic low-frequency extrapolation.

A generative foundation model for an all-in-one seismic processing framework A self-supervised learning framework for seismic low-frequency extrapolation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.497712Z

Source-reported events for the cited work

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Observation c9a42e02-f62f-49af-ad15-e7a1c465c54a · outbound

This paper cites Storseismic: A new paradigm in deep learning for seismic processing.

A generative foundation model for an all-in-one seismic processing framework Storseismic: A new paradigm in deep learning for seismic processing

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.488783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c513252-a4c6-4d44-9389-f980807fb901 · outbound

This paper cites Seismic Foundation Model (SFM): a new generation deep learning model in geophysics.

A generative foundation model for an all-in-one seismic processing framework Seismic Foundation Model (SFM): a new generation deep learning model in geophysics

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.141893Z

Source-reported events for the cited work

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Observation 970cb328-98fc-42ff-9c4c-ccca77fb4a3c · outbound

This paper cites Meta-processing: A robust framework for multi-tasks seismic processing.

A generative foundation model for an all-in-one seismic processing framework Meta-processing: A robust framework for multi-tasks seismic processing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.478984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ab5b19cb-8408-4d42-8e12-879f9737fe83 · outbound

This paper cites Conditional denoising diffusion probabilistic model for ground-roll attenuation.

A generative foundation model for an all-in-one seismic processing framework Conditional denoising diffusion probabilistic model for ground-roll attenuation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.468648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac222946-8d47-4f52-864d-e8ecf850b6cf · outbound

This paper cites Diffusion models for multidimensional seismic noise attenuation and superresolution.

A generative foundation model for an all-in-one seismic processing framework Diffusion models for multidimensional seismic noise attenuation and superresolution

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.457889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6d4a9dcd-8b57-43b9-85ef-7be00db9ec1f · outbound

This paper cites Cold diffusion model for seismic denoising.

A generative foundation model for an all-in-one seismic processing framework Cold diffusion model for seismic denoising

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.446920Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f1df9dde-bd73-4218-9cd5-60f7d23429f6 · outbound

This paper cites Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling.

A generative foundation model for an all-in-one seismic processing framework Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-09T16:36:42.285261Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5a7b081-1c91-408b-a8a1-555e253dfe78 · outbound

This paper cites Generative interpolation via a diffusion probabilistic model.

A generative foundation model for an all-in-one seismic processing framework Generative interpolation via a diffusion probabilistic model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.436461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f220f3a-2fb2-41b5-a213-f5bcf9383ff6 · outbound

This paper cites Self-Supervised Diffusion Model for 3-D Seismic Data Reconstruction.

A generative foundation model for an all-in-one seismic processing framework Self-Supervised Diffusion Model for 3-D Seismic Data Reconstruction

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-09T16:36:42.269650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6aa08959-ed04-41fa-9498-0ca0959dae71 · outbound

This paper cites Seismic data interpolation via denoising diffusion implicit models with coherence-corrected resampling.

A generative foundation model for an all-in-one seismic processing framework Seismic data interpolation via denoising diffusion implicit models with coherence-corrected resampling

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.425596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 558ba85e-e4c6-49ec-aced-9a208143dca7 · outbound

This paper cites Seisresodiff: Seismic resolution enhancement based on a diffusion model.

A generative foundation model for an all-in-one seismic processing framework Seisresodiff: Seismic resolution enhancement based on a diffusion model

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.415151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation edad118b-b578-4dcb-adfb-f55230a1400c · outbound

This paper cites Conditional denoising diffusion probabilistic model for seismic diffraction separation and imaging.

A generative foundation model for an all-in-one seismic processing framework Conditional denoising diffusion probabilistic model for seismic diffraction separation and imaging

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.405153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7654684e-97a2-4cbc-9785-cb701ce2af6f · outbound

This paper cites Generative diffusion model for seismic imaging improvement of sparsely acquired data and uncertainty quantification.

A generative foundation model for an all-in-one seismic processing framework Generative diffusion model for seismic imaging improvement of sparsely acquired data and uncertainty quantification

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.174334Z

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Observation f9fadc62-a505-4647-9f08-145b35bd66af · outbound

This paper cites A prior regularized full waveform inversion using generative diffusion models.

A generative foundation model for an all-in-one seismic processing framework A prior regularized full waveform inversion using generative diffusion models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.389224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 89c1276b-6531-4975-bb13-f2d2afd172f3 · outbound

This paper cites Controllable seismic velocity synthesis using generative diffusion models.

A generative foundation model for an all-in-one seismic processing framework Controllable seismic velocity synthesis using generative diffusion models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.379490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b27f38c-6c59-48ac-a6e0-028ef911bbf0 · outbound

This paper cites Learned regularizations for multi-parameter elastic full waveform inversion using diffusion models.

A generative foundation model for an all-in-one seismic processing framework Learned regularizations for multi-parameter elastic full waveform inversion using diffusion models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.370081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a5818bb-c5ae-4e3b-8f3c-21b63cb038d4 · outbound

This paper cites Deep diffusion models for seismic processing.

A generative foundation model for an all-in-one seismic processing framework Deep diffusion models for seismic processing

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.360438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ff6cb17-cb08-4dc8-9290-e7ad89799c85 · outbound

This paper cites Denoising diffusion probabilistic models.

A generative foundation model for an all-in-one seismic processing framework Denoising diffusion probabilistic models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.190632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73deec6f-a8db-4ab2-a7fc-0ab873a0816c · outbound

This paper cites Denoising Diffusion Implicit Models.

A generative foundation model for an all-in-one seismic processing framework Denoising Diffusion Implicit Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.194178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d858d43e-f827-477f-8a25-6825f961942b · outbound

This paper cites Cold diffusion: Inverting arbitrary image transforms without noise.

A generative foundation model for an all-in-one seismic processing framework Cold diffusion: Inverting arbitrary image transforms without noise

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.198040Z

Source-reported events for the cited work

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Observation 5a32a0a9-65aa-43f8-9056-988b8e2b8492 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

A generative foundation model for an all-in-one seismic processing framework High-resolution image synthesis with latent diffusion models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.201509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b42912f6-e06b-4b56-abb4-1e0bf2f376ca · outbound

This paper cites Optimizing a transformer-based network for a deep learning seismic processing workflow.

A generative foundation model for an all-in-one seismic processing framework Optimizing a transformer-based network for a deep learning seismic processing workflow

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.332247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95fd3720-b1f0-432f-acde-90c7d3c1bf50 · outbound

This paper cites Attention is all you need.

A generative foundation model for an all-in-one seismic processing framework Attention is all you need

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.207887Z

Source-reported events for the cited work

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Observation cf6ecbb4-a2d6-4000-bdbe-403413edc625 · outbound

This paper cites Multi-task learning for low-frequency extrapolation and elastic model building from seismic data.

A generative foundation model for an all-in-one seismic processing framework Multi-task learning for low-frequency extrapolation and elastic model building from seismic data

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.315832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aece70bf-35a3-4d81-8e21-aa8ba7ff4397 · outbound

This paper cites Deepwave, September 2023.

A generative foundation model for an all-in-one seismic processing framework Deepwave, September 2023

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T16:36:42.214081Z

Source-reported events for the cited work

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Observation c39ce678-535c-4704-ac38-2c575e7b9232 · outbound

This paper cites Formation velocity and density—the diagnostic basics for stratigraphic traps.

A generative foundation model for an all-in-one seismic processing framework Formation velocity and density—the diagnostic basics for stratigraphic traps

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:36:42.305669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation d53af2b2-3db2-4b27-ac98-41fa9172cb0f · inbound

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems cites this paper.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems A generative foundation model for an all-in-one seismic processing framework

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:41:27.024032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.