Crust Macrofracturing as the Evidence of the 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 the 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 of the crust. Analysis of the structure of this area leads us to the conclusion that macrofracturing was caused by the last deglaciation.
- Publication:
-
Pure and Applied Geophysics
- Pub Date:
- September 2023
- DOI:
- 10.1007/s00024-023-03334-7
- arXiv:
- arXiv:2206.02652
- Bibcode:
- 2023PApGe.180.3289A
- Keywords:
-
- Machine learning;
- KNN;
- fennoscandia;
- earth crust;
- moho boundary shape;
- low S-velocity layer;
- glacial isostatic adjustment;
- post-glacial rebound;
- elastic rebound;
- receiver function;
- Physics - Geophysics;
- Computer Science - Machine Learning;
- 86-08;
- J.2
- E-Print:
- doi:10.1007/s00024-023-03334-7