Automatic processing of seismic data is today a key element in the efforts to achieve high quality seismic systems. Automated procedures for locating seismic events with a network including arrays and single element seismometers usually incorporate back-azimuth estimates, arrival-time data, and associated uncertainties into a least-squares-inverse location algorithm. Such an algorithm is quite cumbersome and requires expanding a set of non-linear equations in a Taylor series. Second-order terms usually not included in the algorithm can be important if the initial estimate is far from the solution.

We propose to use elastic neural nets (ENN) to find the initial estimation in automated procedures of locating seismic events and discuss the results for simulated seismic events. The advantages of ENN are the simplicity of the algorithm, the fast convergence and the high efficiency.