Vibroseis arrays consisting of multiple trucks are typically used to acquire reflection seismic data to increase the amount of P-wave energy imparted to the earth and reduce surface wave energy in the recorded signal. Vibroseis trucks are beneficial with respect to reducing peak levels of shaking when compared to explosives, and are often used in urban areas to reduce vibrations. Nevertheless, ground vibrations are still of concern when using Vibroseis trucks, and are typically monitored using a particle velocity meter. Measured values of Peak Particle Velocity (PPV) are compared to vibration standards to assess whether or not structures are being exposed to excessive vibrations. Numerical studies have demonstrated that PPV levels surrounding Vibroseis arrays vary because of constructive and destructive interference patterns between the individual trucks in the array, and that caution is warranted when identifying appropriate monitoring points to capture the highest PPVs generated by the array. To identify potential points of high PPV in the field, a rigorous and highly specialized numerical analysis would be required. As an alternative, a probabilistic approach based on existing PPV data may be employed. Herein, such an approach is implemented by performing statistical analyses of experimental and numerical PPV datasets. Analysis of the field data reveals that probability of exceeding a given PPV can be calculated as a function of distance from the array. Analysis of synthetic numerical data reveals similar results, but variability in PPV is less for the numerical data and the numerical analysis tends to overestimate PPV. This difference is attributed to variables in site conditions and ground coupling that cannot be reliably simulated in an idealized numerical model. Analyses such as those presented herein may be useful in quantifying and mitigating the risk associated with vibration exposure caused by Vibroseis exploration to existing structures.