Time Reverse Imaging (TRI) is evolving into a standard technique for localizing and characterizing seismic events. In recent years, TRI has been applied to a wide range of applications from the lab scale over the field scale up to the global scale. No identification of events and their onset times is necessary when localizing events with TRI. Therefore, it is especially suited for localizing quasi-simultaneous events and events with a low signal-to-noise ratio. However, in contrast to more regularly applied localization methods, the prerequisites for applying TRI are not sufficiently known. To investigate the significance of station distributions, complex velocity models and signal-to-noise ratios for the localization quality, numerous simulations were performed using a finite difference code to propagate elastic waves through three-dimensional models. Synthetic seismograms were reversed in time and re-inserted into the model. The time-reversed wavefield backpropagates through the model and, in theory, focuses at the source location. This focusing was visualized using imaging conditions. Additionally, artificial focusing spots were removed with an illumination map specific to the setup. Successful localizations were sorted into four categories depending on their reliability. Consequently, individual simulation setups could be evaluated by their ability to produce reliable localizations. Optimal inter-station distances, minimum apertures, relations between array and source location, heterogeneities of inter-station distances and total number of stations were investigated for different source depth as well as source types. Additionally, the quality of the localization was analysed when using a complex velocity model or a low signal-to-noise ratio. Finally, an array in Southern California was investigated for its ability to localize seismic events in specific target depths while using the actual velocity model for that region. In addition, the success rate with recorded data was estimated. Knowledge about the prerequisites for using TRI enables the estimation of success rates for a given problem. Furthermore, it reduces the time needed for adjusting stations to achieve more reliable localizations and provides a foundation for designing arrays for applying TRI.