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\begin{document}

\title{Earthquake Aftershocks Pattern Prediction}



\author[1]{Alemayehu Belay Kassa}%
\author[1]{Mulugeta Tuji Dugda}%
\author[2]{Line Pouchard}%
\author[2]{Yuewei Lin}%
\author[3]{Amanuel Seifu}%
\affil[1]{Electrical and Computer Engineering, Morgan State University}%
\affil[2]{Computational Science Initiative, Brookhaven National Laboratory}%
\affil[3]{Computer Science, University of Maryland College Park}%


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  \date{May 26, 2024}


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\begin{abstract}
Large earthquakes, especially those occurring in a city or population
centers, create devastation and havoc, and often times kindle several
deaths and injuries, and significant infrastructure damage that lead to
several billions of dollars in losses. Marine earthquakes are the
leading cause of large tsunamis which cause deaths, destruction,
displacement of population, and a possible nuclear meltdown. Thus,
prediction of earthquake or its aftershocks or earthquake early warning
system has a great potential to mitigate the loss of life as well as
different kinds of damage. Earthquake prediction would mean forecasting
the occurrence of an earthquake by providing both its magnitude estimate
and accurate location. Earthquake prediction has been an important area
of seismology research for quite a while, and it looks like it will
continue to be an important area of research. Recently, with the
implementation of deep learning in seismology, scientists have been able
to detect, predict, and model seismic waves and earthquake aftershocks.

Earthquake aftershocks are generally triggered by changes in stress
formed by large earthquakes that happen within, or surrounding a given
fault network system. The main goal of this study is to investigate the
improvement of aftershock pattern predictions with the implementation of
tuning and optimizing of deep learning parameters. To achieve these
goals, we have developed an algorithm that can help first gather
mainshock-aftershock sequence data. Some of the criteria used in
identifying earthquakes that initiate an aftershock is to look at
earthquakes that happen within a certain radius, the values we attempted
are within about 0.5 degrees range, and within a certain period, from
few seconds to several weeks of the occurrence of the main shock. For
the sequence identification, we have been using seismic data from the
United States Geological Survey (USGS)-National Earthquake Information
Center (NEIC). We are also looking at different open-source data
gathered by researchers for a similar study. The deep neural networks we
are implementing make use of Keras python Toolkit, and Theano and
Tensorflow libraries, with a plan to use PyTorch python library instead
of Theano library in the future because of some maintenance issues. To
this point our attempts have shown a good progress.%
\end{abstract}%



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