Fault imaging at Mt Pollino (Italy) from relative location of microearthquakes

Relative location of microearthquakes that occurred at Mt Pollino (Italy) from 2011 to 2013 have been analyzed with the aim of a detailed imaging of the geometry of active faults. We identified 27 clusters composed of a number of earthquakes from 9 to 33, with local magnitude in the range 0.6–2.7. The relative location shows that the distribution of hypocentres in each cluster is characterized by extension from few tens of meters to at most 350 m. For each cluster the hypocentre distribution was fitted by a plane to infer the fault orientation, and results were compared with the fault plane solutions corresponding to the focal mechanism of earthquakes of the same cluster. The comparison shows a good agreement in most of the cases. The relative location analysis, generally applied to earthquakes with similar waveform, has been improved to permit also the relative location of earthquakes characterized by not similar signals. To achieve this purpose a modified procedure that overcome the condition of very similar waveforms has been applied to estimate the time delay between first pulses of the master events. The relative location of master events of all clusters shows a precise imaging of the relative position of all analysed sources and allows also to follow with high accuracy the evolution in time of the seismic swarm within the selected periods. The hypocentre position of master events and the nearly parallel fitting planes of any clusters suggest that most of the analyzed earthquakes were produced by different patches of the same fault. The final results depict a main fault plane characterized by NW–SE strike, dip of about 35–45◦ and depth between 4.5 and 6.5 km b.s.l. Focal mechanisms, used also to evaluate the local stress field, are mostly of normal type with few strike slip solutions for the shallowest events. This result is in good agreement with the local tectonic stress regime that is characterized by predominant NE–SW transtension, as inferred from structural, seismological and geophysical data.