Velocity models play a key role in locating microseismic events; however, it is usually challenging to construct them reliably. Traditional model-building strategies depend on the availability of well logs or perforation shots. We simultaneously invert for microseismic event locations and a velocity model under the Bayesian inference framework, and we apply it in a field data set acquired in the Vaca Muerta Formation at Neuquén, Argentina. This methodology enables uncertainty and posterior covariance analysis. By matching the moveouts of the P- and S-wave arrival times, we were able to estimate a 1D velocity model to achieve improved event locations. Various analyses indicate the superiority of this model over a model built with the traditional strategy. With this algorithm, we can perform microseismic monitoring to fracturing treatments in which no perforation data are available. In addition, we can also apply it for long-term passive seismicity reservoir monitoring in which changes of reservoir properties are expected.