#7579. Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis

October 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Building and Construction;
Safety, Risk, Reliability and Quality;
Geology;
Civil and Structural Engineering;
Geotechnical Engineering and Engineering Geology;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №7579.1 Contract №7579.2 Contract №7579.3 Contract №7579.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
The subsurface geological cross-section is indispensable before design and construction of a geotechnical structure can commence. The development of geological cross-sections often requires significant manual efforts for simplification of stratigraphic boundaries. For example, straight lines are commonly used to connect stratum boundaries at adjacent boreholes, and soil layers with small thicknesses are often ignored. Such a simplification heavily relies on practitioners’ experience and may induce great uncertainties in the developed geological cross-sections and subsequent geotechnical analysis and design (e.g. slope stability analysis).
Keywords:
machine learning; Multiple-point statistics; slope stability; sparse measurements

Contacts :
0