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\begin{document}

\title{The Possibility of Earthquake Forecasting: Learning from Nature}



\author[1]{Dimitar Ouzounov}%
\author[2]{Sergey Pulinets}%
\affil[1]{Chapman University, Orange, CA}%
\affil[2]{Space Research Institute, RAS, Moscow}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\begin{abstract}
We present approach based on physically substantiated short-term
precursor's selection to provide the real-time multiparameter monitoring
having as a purpose the short-term forecast of earthquakes. Three main
principles should be fulfilled: 1. We register natural phenomena for
which we have multi-year experience demonstrating their connection with
the earthquake preparation process 2. All these phenomena have very high
statistical confidence in relation to post-phenomena earthquake
occurrence 3. We have the physical explanation of these anomalous
phenomena generation and their interrelation within the framework of
earthquake preparation process. It is important to note that precursors
are registered in different geospheres (lithosphere, atmosphere,
ionosphere and magnetosphere) what confirms the fact of geospheres
coupling during the earthquake preparation period. For the first time we
included in consideration the role of triggers and their relationship
with precursors. One of the well confirmed triggers is the Space weather
evens changing the global atmospheric circulation. The arising
large-scale irregularities of atmospheric pressure may serve as
earthquake triggers while their linear borders coincide with the active
tectonic fault. Another recently revealed effect is existence of
earthquake retarders when earthquake happens later (up to month) in
comparison with our procedure of earthquake time determination.
Complications created by triggers and retarders in developed procedure
of short-term earthquake forecast should be resolved in future
development of our research. All these ideas are presented in the book
which just published by Institute of Physics (IOP) and has the same
title as abstract. This work was supported by RSF (project No
18-12-00441)%
\end{abstract}%



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