  A sparse modeling approach is proposed for analyzing scanning tunneling
microscopy topography data, which contains numerous peaks corresponding to
surface atoms. The method, based on the relevance vector machine with
$\mathrm{L}_1$ regularization and $k$-means clustering, enables separation of
the peaks and atomic center positioning with accuracy beyond the resolution of
the measurement grid. The validity and efficiency of the proposed method are
demonstrated using synthetic data in comparison to the conventional
least-square method. An application of the proposed method to experimental data
of a metallic oxide thin film clearly indicates the existence of defects and
corresponding local lattice deformations.
