ARXIV:2605.11109 · SEISMIC DATA DENOISING · SUBMITTED 13 MAY · 21:00 UTC · FRESHNESS STALE

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Deploying Self-Supervised Learning for Real Seismic Data Denoising

Giovanny A. M. Arboleda · Claudio D. T. de Souza · Carlos E. M. dos Anjos · Lessandro de S. S. Valente · Roosevelt de L. Sardinha · Albino Aveleda · +3 at arXiv

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Ship in 2-4 weeks›Score6.0Evidence unverified

Opportunity summary

Pain A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data. In this work, the deployment of the Noisy-as-Clean (NaC) method was evaluated for…

METHOD

Full abstract

Self-supervised learning (SSL) has emerged as a promising approach to seismic data denoising as it does not require clean reference data. In this work, the deployment of the Noisy-as-Clean (NaC) method was evaluated for real seismic data denoising under controlled conditions. Two independent seismic acquisitions, each comprising noisy and filtered data, were organized into four real datasets. The NaC SSL method was adapted to add real noise to the noisy input, controlled by a parameter. An experimental protocol with ten experiments was designed to compare different strategies for deploying the NaC SSL method with the supervised learning baseline, using identical network topology and hyperparameters. The models were evaluated in terms of denoising performance, computational cost, and generalization capability. The results show that the synthetic additive white Gaussian noise (AWGN) is inadequate for the denoising of seismic data within the NaC method, and performance strongly depends on the compatibility between the injected and actual noise characteristics. Furthermore, both the characteristics of the seismic data and the noise level influence the performance of the model. Self-supervised fine-tuning on test data has improved SSL performance, whereas no such gain was observed for fine-tuning of supervised models. Finally, NaC has shown to be a simple, effective, and model-independent method that offers a feasible solution for the denoising of real seismic data.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. The results show that the synthetic additive white Gaussian noise (AWGN) is inadequate for the denoising of seismic data within the NaC method, and…

WHY NOW

Seismic Data Denoising moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.

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Opportunity summary

Score6.0

PainA self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Deploying Self-Supervised Learning for Real Seismic Data Denoising

Giovanny A. M. Arboleda · Claudio D. T. de Souza · Carlos E. M. dos Anjos · Lessandro de S. S. Valente · Roosevelt de L. Sardinha · Albino Aveleda · +3 at arXiv

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Competitive landscape

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Segment

Seismic Data Denoising

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A self-supervised learning method (NaC) adapted for real seismic data denoising, demonstrating effectiveness and improved performance through fine-tuning on test data.

Segment

Seismic Data Denoising

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Deploying Self-Supervised Learning for Real Seismic Data Denoising

Deploying Self-Supervised Learning for Real Seismic Data Denoising

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