Seismic Signal Denoising and Decomposition Using Deep Neural Networks

By Weiqiang Zhu, S. M. Mousavi, Gregory C. Beroza. IEEE Transactions on Geoscience and Remote Sensing, 2018

406 citations recorded by the source corpus.

Frequency filtering is widely used in routine processing of seismic data 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 simultaneously learn 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. Because the noise statistics are automatically learned from data and require no assumptions, our method properly handles white noise, 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 the 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 the 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.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/248caa1cd0f2bbb6b5b4fc2550e85b622ce51ef7

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Citations in the corpus

Deep-learning seismology (2022, background): “…data, including the random noise ( 35 ), coherent low-frequency noise ( 36 ), swell noise ( 37 ), ground roll ( 38 ), and coherent and incoherent noise ( 32 ) in conventional and unconventional seismic data such as DAS ( 39 ) and in one-dimensional (1D) ( 32 ), 2D ( 40 ), and 3D ( 41 ) recordings.”
Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions (2021, background): “, 2015) and seismic signal denoising (Zhu et al., 2019).”
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, positive): “Examples of applications of machine learning methods for denoising seismic signals include both supervised [29] and unsupervised [30]–[33] methods.”
Semi-Supervised DAS VSP Data Denoising Using Signal and Noise Distribution Difference (2024, positive): “In recent years, deep learning methods have been proven to be a promising tool for denoising seismic data [17], [18], [19], [20], [21], [22], [23].”
Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “For example, U-Net-based DeepDenoiser can separate signals and noise by learning a nonlinear regression (Zhu et al., 2019).”