Characteristics of slip-rate variability and temporal earthquake clustering across a distributed network of active normal faults from in situ 36Cl cosmogenic dating of fault scarp exhumation.

We present an in situ 36Cl dataset recording the exhumation of 27 active normal fault planes by earthquake slip for the central Apennines, Italy. We do this to constrain the characteristics of earthquake clustering and anticlustering across the entire extending orogen, and in an attempt to constrain the reasons why clustering and anticlustering occurs. We show that duration and magnitude of clustering and anticlustering, and their characteristics, can be explained by a model where the transfer of differential stress between faults and their underlying shear-zones, and between neighbouring fault/shear-zone structures, produces changes in strain-rates on underlying viscous shear zones which drive periods of rapid or reduced slip-rate on their overlying faults. We suggest that stress increase on an underlying shear zone produced by coseismic slip on its overlying fault could be the mechanism that initiates an earthquake cluster. We suggest that stress reductions on shear-zones from coseismic slip located across strike could be the mechanism that initiates an earthquake anticluster. The durations of anticlusters are controlled by the summed stress decreases through time on shear zones, because although these shear zones are slipping relatively slowly, eventually they will load their overlying fault to failure initiating a new cluster, with anticlusters induced across strike. Thus, there is dynamic feedback both up and down dip between faults and their underlying shear zones and crucially across strike between neighbouring fault/shear-zone structures. If the dynamics producing clustering and anticlustering can be constrained, it may be that observations of these phenomena should be included in probabilistic seismic hazard assessments (PSHA) and also interpretations of regional deformation rates and crustal rheologies based on geodetic data. Multi-millennial clustering and anticlustering should become a subject for discussion in these scientific communities.