Application of Machine Learning Algorithms in Characterisation of Sonic Wave Velocities within Various Geological Formations

This thesis investigates the applicability of Machine Learning (ML) algorithms and hybrid models in characterising compressional (VP) and shear wave velocities (VS) to address their limited availability in the mining industry. The primary objective is to evaluate the suitability of ML algorithms for creating predictive models for spatially variable data, specifically VP and VS, while exploring alternative or assisting approaches to enhance prediction accuracy.