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\begin{document}

\title{A magnetic data correction workflow for sparse, four dimensional data}



\author[1,1]{Alan R.A. Aitken}%
\author[1,1]{Lara Nigro Ramos}%
\author[2,2]{Jason L Roberts}%
\author[3,3]{Jamin Stevens Greenbaum}%
\author[2,2]{Lenneke M Jong}%
\author[3,3]{Duncan Alexander Young}%
\author[3,3]{Donald D Blankenship}%
\affil[1]{University of Western Australia}%
\affil[2]{Australian Antarctic Division}%
\affil[3]{University of Texas at Austin}%


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  \date{May 20, 2024}


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\begin{abstract}
High-quality aeromagnetic data are important in guiding new knowledge of
the solid earth in frontier regions, such as Antarctica, where these
data are often among the first data collected. The difficulties of data
collection in remote regions often lead to less than ideal data
collection, leading to data that are sparse and four-dimensional in
nature. Standard aeromagnetic data collection procedures are optimised
for the (nearly) 2D data that are collected in industry-standard
surveys. In this work we define and apply a robust magnetic data
correction approach that is optimised to these four dimensional data.
Data are corrected in three phases, first with operations on point data,
correcting for spatio-temporal geomagnetic conditions, then operations
on line data, adjusting for elevation differences along and between
lines and finally a line-based levelling approach to bring lines into
agreement while preserving data integrity. For a large-scale East
Antarctic survey, the overall median cross-tie error reduction error
reduction is 93\%, reaching a final median error of 5 nT. Error
reduction is are spread evenly between phase 1 and phase 3 levelling
operations. Phase 2 does not reduce error directly but permits a
stronger error reduction in phase 3. Residual errors are attributed to
limitations in the ability to model 4D geomagnetic conditions and also
some limitations of the inversion process used in phase 2. Data have
improved utility for geological interpretation and modelling, in
particular quantitative approaches, which are enabled with less bias and
more confidence.%
\end{abstract}%



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