UCLA

The concept of background seismicity is strictly related to the identification of spontaneous and triggered earthquakes. The definition of foreshocks, main shocks and aftershocks is currently based on procedures depending on parameters whose values are notoriously assumed by subjective criteria. We propose a method for recognizing the background and the induced seismicity statistically. Rather than using a binary distinction of the events in these two categories, we prefer to assign to each of them a probability of being independent or triggered. This probability comes from an algorithm based on the ETAS model. A certain degree of subjectivity is still present in this procedure, but it is limited by the possibility of adjusting the free parameters of the algorithm by rigorous statistical criteria such as maximum likelihood. We applied the method to the seismicity of southern California and analyzed the sensitivity of the results to the free parameters in the algorithm. Finally, we show how our statistical declustering algorithm may be used for mapping the background seismicity, or the moment rate in a seismic area.