  Galaxy mergers play a crucial role in galaxy evolution. However, the
correlation between mergers and the local environment of galaxies is not fully
understood. We aim to address the question of whether galaxy mergers prefer
denser or less dense environments by quantifying the spatial clustering of
mergers and non-mergers. We use two different indicators to classify mergers
and non-mergers - classification based on a deep learning technique ($f$) and
non-parametric measures of galaxy morphology, Gini-$M_{20}$ ($g$). We used a
set of galaxy samples in the redshift range $0.1 < z < 0.15$ from the Galaxy
and Mass Assembly (GAMA) survey with a stellar mass cut of $\log
(M_{\star}/M_{\odot} ) > 9.5$. We measured and compared the two-point
correlation function (2pCF) of mergers and non-mergers classified using the two
merger indicators $f$ and $g$. We measured the marked correlation function
(MCF), in which the galaxies are weighted by $f$ to probe the environmental
dependence of galaxy mergers. We do not observe a statistically significant
difference between the clustering strengths of mergers and non-mergers obtained
using 2pCF. However, using the MCF measurements with $f$ as a mark, we observe
an anti-correlation between the likelihood of a galaxy being a merger and its
environment. Our results emphasise the advantage of MCF over 2pCF in probing
the environmental correlations. Based on the MCF measurements, we conclude that
the galaxy mergers prefer to occur in the under-dense environments on scales $>
50 \, h^{-1} \mathrm{kpc}$ of the large-scale structure (LSS). We attribute
this observation to the high relative velocities of galaxies in the densest
environments that prevent them from merging.
