  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.
