Recently, with the increase of dense seismic monitoring networks and the resultant massive seismic data all over the world (e.g., Groningen gas reservoir), the traditional geophysical algorithms have faced the “big data” and high dimensionality issues and become inefficient and costly. Deep learning, as a candidate to mitigate the “big data” and high dimensionality problems, has started to be applied to many geophysical problems; however, previous deep learning studies seldom consider the model uncertainty, which may seriously impair reservoir production estimates, ground motion predictions, and seismic early warnings. Bayesian neural networks provide a practical solution to solve the uncertainty quantification problems in deep learning, i.e., to make AI safe. In this paper, we construct a Bayesian convolutional neural network and implement a stochastic regularized technique – dropout – to quantify the uncertainty of seismic location. This method has been applied to induced seismicities in the Groningen gas field in the Netherlands.