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\begin{document}

\title{Denoising 3-component seismic data using deep neural network}



\author[1]{Jiuxun Yin}%
\author[2]{Marine Denolle}%
\author[3]{Bing He}%
\affil[1]{Harvard University}%
\affil[2]{University of Washington}%
\affil[3]{University of Rhode Island}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
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
( \textless{} 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.%
\end{abstract}%



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