  Machine learning methods were applied to reconsider the results of several
passive seismic experiments in Finland. We created datasets from different
stages of the receiver function technique and processed them with one of basic
machine learning algorithms. All the results were obtained uniformly with the
$k$-nearest neighbors algorithm. The first result is the Moho depth map of the
region. Another result is the delineation of the near-surface low $S$-wave
velocity layer. There are three such areas in the Northern, Southern, and
central parts of the region. The low $S$-wave velocity in the Northern and
Southern areas can be linked to the geological structure. However, we attribute
the central low $S$-wave velocity area to a large number of water-saturated
cracks in the upper 1-5 km. Analysis of the structure of this area leads us to
the conclusion that macrofracturing was caused by the last deglaciation.
