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\begin{document}

\title{Appraisal of Resistivity Inversion Models with Convolutional Variational
Encoder-Decoder Network}



\author[1]{Bibin Wilson }%
\author[1]{Anand Singh }%
\author[1]{Amit Sethi }%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 19, 2024}


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\begin{abstract}
Recovering the actual subsurface electrical resistivity properties from
the electrical resistivity tomography data is challenging because the
inverse problem is nonlinear and ill-posed. This paper proposes a
Variaional Encoder-Decoder (VED) based network to obtain resistivity
model, which maps the apparent resistivity data(input) to true
resistivity data(output). ~Since deep learning models are highly
dependent on training sets and providing a meaningful geological
resistivity model is complex, we have first developed a method to
construct many realistic resistivity synthetic models. Our algorithm
automatically constructs an apparent resistivity pseudo-section from
these resistivity models. We further computed the resistivity from two
different neural architectures for comparison -- UNet, and attention
UNet with and without input depth encoding apparent data. In the end, we
have compared our deep learning results with traditional inversion and
borewell data on apparent resistivity datasets collected for aquifer
mapping in the hard rock terrain of the West Medinipur district of West
Bengal, India. A detailed qualitative and quantitative evaluation
reveals that our VED approach is the most effective for the inversion
compared to other networks considered.%
\end{abstract}%



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