Seismic Signal Denoising and Decomposition Using Deep Neural Networks
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Abstract
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…
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3Topics & keywords
Topics
Keywords
- Computer science
- Noise reduction
- Noise (video)
- Passive seismic
- Preprocessor
- Signal-to-noise ratio (imaging)
- SIGNAL (programming language)
- Artificial neural network
UN Sustainable Development Goals
- Sustainable cities and communities
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