\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Nowcasting Earthquakes: Imaging the Earthquake Cycle in California with
Machine Learning}



\author[1]{John B. Rundle}%
\author[2]{Andrea Donnellan}%
\author[3]{Geoffrey Fox}%
\author[4]{James P Crutchfield}%
\author[5]{Robert A Granat}%
\affil[1]{University of California - Davis}%
\affil[2]{Jet Propulsion Laboratory, California Institute of Technology \& University of Southern California}%
\affil[3]{Indiana University Bloomington}%
\affil[4]{University of California}%
\affil[5]{City College of New York}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
We propose a new machine learning-based method for nowcasting
earthquakes to image the time-dependent earthquake cycle. The result is
a timeseries which may correspond to the process of stress accumulation
and release. The timeseries is constructed by using Principal Component
Analysis of regional seismicity. The patterns are found as eigenvectors
of the cross-correlation matrix of a collection of seismicity timeseries
in a coarse grained regional spatial grid (pattern recognition via
unsupervised machine learning). The eigenvalues of this matrix represent
the relative importance of the various eigenpatterns. Using the
eigenvectors and eigenvalues, we then compute the weighted correlation
timeseries (WCT) of the regional seismicity. This timeseries has the
property that the weighted correlation generally decreases prior to
major earthquakes in the region, and increases suddenly just after a
major earthquake occurs. As in a previous paper (Rundle and Donnellan,
2020), we find that this method produces a nowcasting timeseries that
resembles the hypothesized regional stress accumulation and release
process characterizing the earthquake cycle. We then address the problem
of whether the timeseries contains information regarding future large
earthquakes. For this we compute a Receiver Operating Characteristic and
determine the decision thresholds for several future time periods of
interest (optimization via supervised machine learning). We find that
signals can be detected that can be used to characterize the information
content of the timeseries. These signals may be useful in assessing
present and near-future seismic hazard.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

