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\begin{document}

\title{Deciphering earthquake triggering mechanisms for induced seismicity
using a fully coupled poroelastic model and machine learning analysis}



\author[1]{Ryley Graham Hill}%
\author[2]{Daniel T. Trugman}%
\author[3]{Matthew Weingarten}%
\affil[1]{University of California, San Diego}%
\affil[2]{University of Nevada, Reno}%
\affil[3]{San Diego State University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
In areas of induced seismicity, earthquakes can be triggered by stress
changes from fluid injection and from static deformation caused by fault
slip. Here we present a method to distinguish between injection-driven
and earthquake-driven triggering of induced seismicity by combining a
calibrated, fully-coupled, poroelastic stress model of wastewater
injection with a random forest machine learning algorithm trained on
both earthquake catalog and modeled stress features. We investigate the
classic Paradox Valley, Colorado induced seismicity dataset as an ideal
test case: a single, high-pressure injector that has induced
\textgreater{}7000 earthquakes between 1991 and 2012. We find that
injection-driven earthquakes are approximately 22\selectlanguage{ngerman}±-5\% of the total
catalog and have distinct spatiotemporal clustering with a larger
b-value, closer proximity to the well and earlier occurrence in the
injection history. Our model may be applicable to other regions to help
determine site's susceptibility to triggered earthquakes due to fluid
injection.%
\end{abstract}\selectlanguage{ngerman}%



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