# The potential of self-supervised networks for random noise suppression in seismic data

arXiv: https://arxiv.org/abs/2109.07344 · HTML: https://mlsanity.com/paper/2109.07344/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [High-Quality Self-Supervised Deep Image Denoising](https://mlsanity.com/paper/1901.10277/) (2019)
- [Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks](https://mlsanity.com/paper/2007.02075/) (2020)
- [MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning](https://mlsanity.com/paper/2109.05294/) (2021)
- [Interpolation and Denoising of Seismic Data using Convolutional Neural Networks](https://mlsanity.com/paper/1901.07927/) (2019)
- [An introduction to distributed training of deep neural networks for segmentation tasks with large seismic datasets](https://mlsanity.com/paper/2102.13003/) (2021)

## Built on by (most cited first, 10 of 10)

- [StorSeismic: A new paradigm in deep learning for seismic processing](https://mlsanity.com/paper/2205.00222/) (2022)
- [A hybrid approach to seismic deblending: when physics meets self-supervision](https://mlsanity.com/paper/2205.15395/) (2022)
- [DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean data](https://mlsanity.com/paper/2304.08120/) (2023)
- [Transfer learning for self-supervised, blind-spot seismic denoising](https://mlsanity.com/paper/2209.12210/) (2022)
- [A generative foundation model for an all-in-one seismic processing framework](https://mlsanity.com/paper/2502.01111/) (2025)
- [Foundation Models For Seismic Data Processing: An Extensive Review](https://mlsanity.com/paper/2503.24166/) (2025)
- [Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising](https://mlsanity.com/paper/2307.06682/) (2023)
- [Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising](https://mlsanity.com/paper/2605.10953/) (2026)
- [Deploying Self-Supervised Learning for Real Seismic Data Denoising](https://mlsanity.com/paper/2605.11109/) (2026)
- [Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness](https://mlsanity.com/paper/2609.14504/) (2026)

## Abstract (arXiv)

Noise suppression is an essential step in any seismic processing workflow. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean data pairs for training. Using blind-spot networks, we redefine the denoising task as a self-supervised procedure where the network uses the surrounding noisy samples to estimate the noise-free value of a central sample. Based on the assumption that noise is statistically independent between samples, the network struggles to predict the noise component of the sample due to its randomnicity, whilst the signal component is accurately predicted due to its spatio-temporal coherency. Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as inversion. To conclude the study, the suggested approach is applied to field data and the results are compared with two commonly used random denoising techniques: FX-deconvolution and Curvelet transform. By demonstrating that blind-spot networks are an efficient suppressor of random noise, we believe this is just the beginning of utilising self-supervised learning in seismic applications.