Whole-body vibration (WBV) occurs when the human body is supported on a surface that is vibrating. Operators and passengers in mobile machinery are exposed to vibration transferred from the seatpan to the body. Excessive vibration exposure is strongly associated with low back pain, therefore seat selection is important for reducing vibration exposure. Recently, we developed an efficient neural network (NN) algorithm that identified the dynamic properties of suspension seats, and then interrogated the models to predict seatpan vertical accelerations for a variety of skidders in the forestry sector. We have expanded this approach to evaluate the influence of different seats on the WBV exposures from load-haul-dump vehicles in the underground mining environment. Of the five seat models that we tested, our results demonstrated that one particular seat model was best able to attenuate vibrations based on the equivalent daily exposure, Λ (8), and the corresponding working hours to reach the upper limit of the ISO 2631-1 health guidance caution zone for 8hour operation. We performed a sensitivity analysis to evaluate the influence of the individual vibration frequency components on the Λ (8) results for each of the seat models. This analysis revealed that each of the industrial seats responded differently to specific vibration frequencies and explained why the seat selection algorithm matched particular seats to specific vibration environments.