Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2506.12897.

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

pith.paper-citation-record.v1
2506.12897 v1

Coverage vector

measured 46 of 46 reference resolution

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:58:04.343729Z

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation c13fc04b-9c15-4f70-adab-ffcc047e7b68 · outbound

This paper cites an unresolved cited work.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:41:27.429045Z

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Observation f764e01a-3f78-4351-b27f-ad9cfec93dea · outbound

This paper cites Learning to decouple and generate seismic random noise via invertible neural network.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Learning to decouple and generate seismic random noise via invertible neural network

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.419858Z

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Observation fca5ce1d-6955-4d24-8b01-7e4988803fbe · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Deep-learning-based seismic data interpolation: A preliminary result

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.410195Z

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Observation dbdc947b-5d5e-47da-ab2a-5a2b582921f9 · outbound

This paper cites Seismic data interpolation using deep learning with generative adversarial networks.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic data interpolation using deep learning with generative adversarial networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.399588Z

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Observation f9c247e1-9e94-4374-98c9-96650376ce95 · outbound

This paper cites Deep-learning tomography.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Deep-learning tomography

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.389053Z

Source-reported events for the cited work

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Observation efc14f83-a7d0-4968-a2e2-8bd44831ee21 · outbound

This paper cites Seismic noise attenuation using unsupervised sparse feature learning.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic noise attenuation using unsupervised sparse feature learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.378929Z

Source-reported events for the cited work

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Observation ea345b71-046f-4523-ad27-b3e3665f838a · outbound

This paper cites Porosity and permeability prediction using a transformer and periodic long short-term network.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Porosity and permeability prediction using a transformer and periodic long short-term network

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.369258Z

Source-reported events for the cited work

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Observation e30a3c03-3131-41b5-ab1c-154bed72c3f5 · outbound

This paper cites Frequency-dependent avo inversion and application on tight sandstone gas reservoir prediction using deep neural network.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Frequency-dependent avo inversion and application on tight sandstone gas reservoir prediction using deep neural network

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.357261Z

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Observation 51ec0c44-c28a-4434-92e3-8ee579ee0d86 · outbound

This paper cites Seismic fault detection with convolutional neural network.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic fault detection with convolutional neural network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.347403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d6d20ec-728e-4c32-8499-72d576485b2c · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.338062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e21205d-788c-49a3-9fad-0da98947e257 · outbound

This paper cites Snips: Solving noisy inverse problems stochastically.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Snips: Solving noisy inverse problems stochastically

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.328881Z

Source-reported events for the cited work

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Observation d93d263c-ef15-4ac2-b81e-eda49b318546 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Score-Based Generative Modeling through Stochastic Differential Equations

Reference 12

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Observation 0a4e6a94-b047-481b-9fb8-61e99d32700b · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Generative modeling by estimating gradients of the data distribution

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.319445Z

Source-reported events for the cited work

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Observation 83ed72f8-bd6c-4086-a974-567a65b88868 · outbound

This paper cites Denoising diffusion probabilistic models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Denoising diffusion probabilistic models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.878318Z

Source-reported events for the cited work

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Observation bda712ed-a00c-4f34-905a-6e4421883e57 · outbound

This paper cites Generative modeling of seismic data using score-based generative models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Generative modeling of seismic data using score-based generative models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.305560Z

Source-reported events for the cited work

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Observation f3203c34-3a59-47d9-bd5a-ce90350f6385 · outbound

This paper cites Generative modeling of seismic data using diffusion models and its application to multi-purpose seismic inverse problems.

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.296118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc3d7be5-2d85-4609-8000-8fa0b644db07 · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Controllable seismic velocity synthesis using generative diffusion models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.287380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 366e2ad9-a4f0-45fa-acaf-01230c74ec95 · outbound

This paper cites Analysis of das seismic noise generation and elimination process based on mean-sde diffusion model.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Analysis of das seismic noise generation and elimination process based on mean-sde diffusion model

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.278127Z

Source-reported events for the cited work

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Observation f221aed9-8605-4004-9872-854de5a25186 · outbound

This paper cites Seismic data strong noise attenuation based on diffusion model and principal component analysis.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic data strong noise attenuation based on diffusion model and principal component analysis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.268825Z

Source-reported events for the cited work

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Observation a7dffc63-9818-408e-87b2-c7c572356392 · outbound

This paper cites Posterior sampling for random noise attenuation via score-based generative models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Posterior sampling for random noise attenuation via score-based generative models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.259690Z

Source-reported events for the cited work

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Observation cc170823-c50d-44c4-bc12-fde661b515e4 · outbound

This paper cites Fast diffusion model for seismic data noise attenuation.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Fast diffusion model for seismic data noise attenuation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.250405Z

Source-reported events for the cited work

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Observation e33b0a06-c741-4ec4-9a01-5789b008d9c5 · outbound

This paper cites Generative interpolation via a diffusion probabilistic model.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Generative interpolation via a diffusion probabilistic model

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.241716Z

Source-reported events for the cited work

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Observation 51c270e8-e90e-4480-a59c-2fffc40904e9 · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic data interpolation via denoising diffusion implicit models with coherence-corrected resampling

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.232841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab0bd5c5-e9c9-4969-aa88-b29b1ceefa44 · outbound

This paper cites Stochastic solutions for simultaneous seismic data denoising and reconstruction via score-based generative models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Stochastic solutions for simultaneous seismic data denoising and reconstruction via score-based generative models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.223107Z

Source-reported events for the cited work

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Observation 435c6422-06b9-42f2-92ec-7391390fba3e · outbound

This paper cites Seisfusion: Constrained diffusion model with input guidance for 3d seismic data interpolation and reconstruction.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seisfusion: Constrained diffusion model with input guidance for 3d seismic data interpolation and reconstruction

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.213122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0441c8c5-dae3-408b-ad11-1b7f8f9091ec · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Generative diffusion model for seismic imaging improvement of sparsely acquired data and uncertainty quantification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.203130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60f3406b-541c-4773-b79e-cdf422b41570 · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seisresodiff: Seismic resolution enhancement based on a diffusion model

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.194083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 017332d8-37d7-48fa-b3ea-52d812300611 · outbound

This paper cites Diffusion model for das-vsp data denoising.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Diffusion model for das-vsp data denoising

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.183494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 390d04dc-be1c-488f-82a4-204143c32657 · outbound

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

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Conditional denoising diffusion probabilistic model for seismic diffraction separation and imaging

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.173169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6c8438d0-9ba9-4064-a4e1-8c6375b2bcf6 · outbound

This paper cites Deep diffusion models for seismic processing.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Deep diffusion models for seismic processing

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.162981Z

Source-reported events for the cited work

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Observation 03937528-6d73-45dd-a5e5-55d33265b9ee · outbound

This paper cites Unsupervised seismic acoustic impedance inversion based on generative diffusion model.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Unsupervised seismic acoustic impedance inversion based on generative diffusion model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.153781Z

Source-reported events for the cited work

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Observation 904f8394-6a6f-486b-a621-c6405abe02d7 · outbound

This paper cites Conditional score- based diffusion models for bayesian inference in infinite dimensions.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Conditional score- based diffusion models for bayesian inference in infinite dimensions

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.143725Z

Source-reported events for the cited work

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

This paper cites A generative foundation model for an all-in-one seismic processing framework.

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

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87cbbe1c-8b58-4a57-a97b-808af12b256e · outbound

This paper cites Denoising Diffusion Implicit Models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Denoising Diffusion Implicit Models

Reference 34

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Source-reported events for the cited work

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Observation 785867e8-4595-4928-873b-6e23a1d5038c · outbound

This paper cites Improved denoising diffusion probabilistic models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Improved denoising diffusion probabilistic models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.947464Z

Source-reported events for the cited work

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Observation 584ed5f2-d469-4dde-908f-454d460209cd · outbound

This paper cites Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606, 2022.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Denoising diffusion restoration models.Advances in Neural Information Processing Systems, 35:23593–23606, 2022

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.950479Z

Source-reported events for the cited work

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Observation 57df466f-5968-4002-868a-87d69a838c42 · outbound

This paper cites Stochastic solutions for linear inverse problems using the prior implicit in a denoiser.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Stochastic solutions for linear inverse problems using the prior implicit in a denoiser

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.123175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83fb1674-5288-459d-9c3d-429367377a95 · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Diffusion posterior sampling for general noisy inverse problems

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.957548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 174fcf1d-b51f-4f70-a52a-37ac5b51ca96 · outbound

This paper cites Seismic random noise attenuation based on non-iid pixel-wise gaussian noise modeling.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Seismic random noise attenuation based on non-iid pixel-wise gaussian noise modeling

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.108502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 19e55eb6-9cea-4fea-804f-e367bc1bd310 · outbound

This paper cites Improved techniques for training score-based generative models.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Improved techniques for training score-based generative models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.098379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8e7a2f3-ed95-4c12-8d87-ef742e428472 · outbound

This paper cites Unified matrix treatment of the fast walsh-hadamard transform.IEEE Transactions on Computers, 100(11):1142–1146, 1976.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Unified matrix treatment of the fast walsh-hadamard transform.IEEE Transactions on Computers, 100(11):1142–1146, 1976

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.087423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e3cddfaf-88ab-4e61-8cde-3e6d5a54785d · outbound

This paper cites Deep image prior.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Deep image prior

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.077167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32b07a1a-8058-4b83-8dd6-767198063620 · outbound

This paper cites Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.067315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce98df71-b181-47d6-80e3-970589780b68 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems U-net: Convolutional networks for biomedical image segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:26.977747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1e881ee-59f9-4222-87f4-ea14afad5c1c · outbound

This paper cites Signal recovery from random measurements via orthogonal matching pursuit.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Signal recovery from random measurements via orthogonal matching pursuit

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.052203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42f63739-c8c4-4d91-a88f-0ec5f1b23e76 · outbound

This paper cites Discrete cosine transform.

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems Discrete cosine transform

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:41:27.042388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 995d0e18-9af0-48ad-8559-491c7e1daf48 · inbound

Estimation of Elastic Parameters with Guidance-based Diffusion model cites this paper.

Estimation of Elastic Parameters with Guidance-based Diffusion model Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T05:58:04.343729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.