Obtaining reliable source locations with time reverse imaging: limits to array design, velocity models and signal-to-noise ratios
Time reverse imaging (TRI) is evolving into a standard technique for locating and characterising seismic events. In recent years, TRI has been employed for a wide range of applications from the lab scale, to the field scale and up to the global scale. No identification of events or their onset times is necessary when locating events with TRI; therefore, it is especially suited for locating quasi-simultaneous events and events with a low signal-to-noise ratio. However, in contrast to more regularly applied localisation 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 with respect to location accuracy, numerous simulations were performed using a finite difference code to propagate elastic waves through three-dimensional models. Synthetic seismograms were reversed in time and reinserted into the model. The time-reversed wave field back propagates through the model and, in theory, focuses at the source location. This focusing was visualised using imaging conditions. Additionally, artificial focusing spots were removed using an illumination map specific to the set-up. Successful locations were sorted into four categories depending on their reliability. Consequently, individual simulation set-ups could be evaluated by their ability to produce reliable source locations.Optimal inter-station distances, minimum apertures, relations between the array and source locations, heterogeneities of inter-station distances and the total number of stations were investigated for different source depths and source types. Additionally, the accuracy of the locations was analysed when using a complex velocity model or a low signal-to-noise ratio.Finally, an array in southern California was investigated regarding its ability to locate seismic events at 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 to adjust stations to achieve more reliable locations and provides a foundation for designing arrays for applying TRI.