Blasting, as a cost-effective method of rock breakage, assures a size distribution for efficient material handling in surface and
underground mining. An immense level of energy released from explosive charge in blast holes induces plastic deformations
in competent rocks, while the remaining part transforms into destructive ground vibrations. Seismic wave velocity is a major
concern for blasting to mitigate possible human discomfort and structural damage. The common approach is to characterize
the wave propagation within geological units by monitoring trial blasts depending on either conventional or advanced statistical
methods. The scaled distance method and the novel soft computing techniques are commonly used for the prediction
of peak particle velocity. This study explores the limitations of conventional statistics and the widely accepted parameters
controlling blast-induced ground vibrations. The velocity vector direction was analyzed to reveal the effects of geological,
geomechanical, and structural features. Discrepancies of the scaled distance method in predicting the peak particle velocity
due to complex geology and the need for an extensive trial blast database motivate the research for an alternative approach.
The proposed method takes advantage of machine learning for the calibration of the mechanical model and simulates ground
vibrations by a mass–spring–damper system. Blast records from an aggregate quarry and a metal mine were used for validation
purposes. The proposed method offers remarkable improvements in terms of accuracy by reducing prediction errors
down to 0.04 mm/s. Regarding the structural condition of rock mass, the average error is around 4% for relatively massive
rock mass and 20% for complex structural geological conditions.