# Seismic Signal Denoising and Decomposition Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1811.02695 · HTML: https://mlsanity.com/paper/1811.02695/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 21 of 21)

- [SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction](https://mlsanity.com/paper/2309.02320/) (2023)
- [A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study](https://mlsanity.com/paper/2201.12683/) (2022)
- [Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications](https://mlsanity.com/paper/2110.11802/) (2021)
- [DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean data](https://mlsanity.com/paper/2304.08120/) (2023)
- [What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study](https://mlsanity.com/paper/2203.08739/) (2022)
- [S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning](https://mlsanity.com/paper/2212.13523/) (2022)
- [Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition](https://mlsanity.com/paper/2412.17333/) (2024)
- [Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction](https://mlsanity.com/paper/2405.03320/) (2024)
- [DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning](https://mlsanity.com/paper/2501.18423/) (2025)
- [PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency](https://mlsanity.com/paper/2502.11913/) (2025)
- [Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network](https://mlsanity.com/paper/2504.20568/) (2025)
- [Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systems](https://mlsanity.com/paper/2509.14219/) (2025)
- [Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures](https://mlsanity.com/paper/2606.02912/) (2026)
- [RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network](https://mlsanity.com/paper/2307.01725/) (2023)
- [High-Rate Phase Association with Travel Time Neural Fields](https://mlsanity.com/paper/2307.07572/) (2023)
- [PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation](https://mlsanity.com/paper/2411.12636/) (2024)
- [Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression](https://mlsanity.com/paper/2502.14355/) (2025)
- [Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms](https://mlsanity.com/paper/2601.05759/) (2026)
- [3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems](https://mlsanity.com/paper/2603.02149/) (2026)
- [Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation](https://mlsanity.com/paper/2603.17855/) (2026)
- [Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising](https://mlsanity.com/paper/2605.10953/) (2026)

## Abstract (arXiv)

Denoising and filtering are widely used in routine seismic-data-processing to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to learn simultaneously a sparse representation of data in the time-frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any non-seismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Our method properly handles a variety of colored noise and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that potential applications of our approach are not limited to these applications or even to earthquake data, and that our approach can be adapted to diverse signals and applications in other settings.