Uncertainty estimation by probabilistic first arrival time tomography using Markov Chain Monte Carlo sampling

We present several applications of probabilistic first arrival time tomography by Markov Chain Monte Carlo sampling dedicated to uncertainty estimation. In the first part, we introduce a new velocity model parameterization based on Johnson-Mehl tessellation that allows applying probabilistic approach to typical seismic refraction data. We also present results of the tomography to a real data set recorded in the context of hydraulic fracturing and illustrate how the velocity model uncertainties can be properly taken into account when locating seismic events.