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Denoising 3-component seismic data using deep neural network
  • Jiuxun Yin,
  • Marine Denolle,
  • Bing He
Jiuxun Yin
Harvard University

Corresponding Author:[email protected]

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Marine Denolle
University of Washington
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Bing He
University of Rhode Island
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Earthquake signals in seismic data are inevitably contaminated with signals from unwanted sources. Separating noise from earthquake signals can greatly improve the analysis of the seismic data, such as earthquake characterization and ambient noise analysis. In this work, we develop a new auto-encoder to extract transient signals from ambient signals directly in the time domain for 3-component seismograms. We benchmark our architecture development and performance against a time-frequency counterpart (similar to the DeepDenoisier). We explore the generalization of our time-domain denoiser by training on various scales of seismic data. First, we train purely on observed seismograms of local ( < 350 km) events using the STandford EArthquake Dataset (STEAD) data set. Second, we generate a data set of observed seismograms from regional earthquakes (350 km-2000 km), which we complement with seismograms generated by hybrid low-frequency deterministic, high-frequency stochastic synthetic waveforms. We explore the robustness of the denoiser on various noise structures. Finally, we explore the quality of the extracted signals, for earthquake characterization and for ambient noise seismology.