  Electronic states within superconducting vortices hold crucial information
about paring mechanisms and topology. While scanning tunneling
microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to
isolate the intrinsic electronic states from extrinsic effects like subsurface
defects and disorders. We combine STM/S with unsupervised machine learning to
develop a method for screening out the vortices pinned by embedded disorder in
Fe-based superconductors. The approach provides an unbiased way to reveal
intrinsic vortex-core states and may address puzzles on Majorana zero modes.
