Crust Macrofracturing as the Evidence of the Last Deglaciation

TL;DR

This study uses machine learning on passive seismic data to map crustal features in Finland, revealing macrofracturing evidence linked to the last deglaciation event.

Contribution

It applies a uniform machine learning approach to seismic data to identify crustal structures and macrofracturing evidence, a novel use of the k-nearest neighbors algorithm in this context.

Findings

Moho depth map of the region

Identification of low S-wave velocity zones

Linking macrofracturing to last deglaciation

Abstract

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.