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\begin{document}

\title{3D inversion scheme for high-resolution analysis of continental scale
Magnetotelluric data}



\author[1]{Arun Singh}%
\author[2]{Rahul Dehiya}%
\affil[1]{Indian Institute of Technology (ISM) Dhanbad}%
\affil[2]{Indian Institute of Science Education and Research}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\begin{abstract}
This study introduces a novel method for performing 3D inversion of
magnetotelluric (MT) data. The proposed method, referred to as the
radiation boundary scheme, employs a two-step simulation strategy for
the computation of both forward and adjoint responses. One of the key
advantages of the scheme is its ability to handle arbitrarily shaped
inversion domains, thereby optimizing the number of unknown model
parameters by discarding model parameters that are not constrained by
the data. Consequently, it significantly improves accuracy and
computational speed as compared to traditional inversion algorithms. The
effectiveness of the developed algorithm is demonstrated through a
comprehensive analysis of 3D inversion using synthetic and two
continental-scale (SAMTEX and USArray) MT data. The method's efficiency
facilitates a detailed analysis of large-scale MT data inversion.
Through numerical experiments, it is observed that using a coarse mesh
for inversion, the resolution is compromised in the shallower part,
resulting in inferior imaging and, consequently, affecting the
estimation of resistivity value in the deeper subsurface. The detailed
numerical experiments indicate that performing a high-resolution
inversion on a small portion of the survey data utilizing a coarsely
inverted model may run into a local minimum. Hence, caution should be
exercised in employing such an approach. Instead, the investigations
suggest simultaneously executing a high-resolution inversion on the
entire data set. The forward/adjoint problem can be solved with a
two-order higher tolerance as compared to the conventional
finite-difference-based inversion algorithm. Therefore, the proposed
algorithm holds significant value for the MT inversion of large data
sets.%
\end{abstract}%



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