\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{Inverse modeling of turbidity currents using 2D shallow-water model and
neural network toward understanding of development processes of
submarine fans}



\author[1]{Hajime Naruse}%
\affil[1]{Kyoto University}%


\vspace{-1em}



  
  \date{June 18, 2024}


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





\selectlanguage{english}
\begin{abstract}
This study aims to establish an inverse model for reconstructing
properties of turbidity currents from their deposits. Although several
attempts of in-situ measurements have been succeeded, flow properties of
large-scale turbidity currents that build submarine fans remain unclear.
Recurrence interval of turbidites in natural levees or lobes of
submarine fans is generally 100s years, while in-situ observation in
modern submarine canyons reported yearly to monthly occurrence of
turbidity currents. That is, turbidity currents that substantially form
submarine fans may be extremely low frequency and large-scale than
currently observed. To understand the behavior of fan-building flows
occurring with sub-millennial scale recurrence interval, the
quantitative reconstruction of flow conditions from geologic records is
required. However, inversion of flow properties in natural scale has
been impractical because the heavy computational load of 2D or 3D models
of turbidity currents has prevented to perform inversion requiring
iteration of calculations. To this end, we propose a method using 2D
shallow-water model with the deep-learning neural network (DNN). In this
method, a horizontal 2D shallow-water model of turbidity currents was
employed as the forward model. Numerical simulation was repeated to
obtain horizontal distribution of thickness of turbidites under various
initial conditions, and then this synthetic data set was used for
supervised training of DNN. After the training phase finished, DNN
properly estimated initial conditions of turbidity currents (e.g.
initial flow height, velocity, etc.) from artificial test data set that
was also produced from the forward model. In previous methods, the
computational cost of 2D model was too high to be employed as the
forward model for turbidite inversion, while our methodology can
reconstruct initial conditions of turbidity currents instantaneously.
Although production of the training data set requires 1000s times
repetition of calculation, all calculation of forward model can be
perfectly parallelized, so that the problem of calculation efficiency
can be easily solved by using PC cluster. Future application of our
method to actual deposits will contribute to understand the development
processes of submarine channels or lobes in the long term.%
\end{abstract}%



\sloppy




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

