The role of long-range correlations and memory in earthquake occurrence

Seismicity occurs because of strain accumulation in the brittle crust due to the action of tectonic forces. However, this background process is just one of numerous ones affecting earthquake occurrence. In the long run it controls the frequency of large events (Neely et al., 2023); conversely, at our time scales it is likely one of the less impactful. The spatial and temporal evolution of seismic sequences has been proposed to be driven by fluid migration and frictional instability and to be affected by stress perturbations as well as anthropic activities, rheology, structural properties of fault systems and by the tectonic regime (e.g., Rice & Cocco, 2007; Ellsworth, 2013; Ross & al., 2017). Consequently, several empirical relationships have been found connecting properties of seismicity with geophysical observables (pore pressure, differential stress, static friction etc.). We suggest that local variability of some routinely investigated parameters can be clarified considering few all-encompassing physical principles instead of advocating several hardly testable physical mechanisms. To provide an observational example, we analyse a high-definition regional seismic catalogue of seismicity in Central Italy (Tan et al., 2021). We identify a chain of relationships, starting with the properties of inherited structures and tectonic stress gradients directly affecting the fractal properties of fault systems, which, in turn, have an impact on the frequency-magnitude scaling of earthquake. It produces implications for the duration of seismic sequences, complexity of the seismogenic source and the composition of moment tensors (Zaccagnino & Doglioni, 2022). Our approach has the advantage to be valid regardless of the specific rheology, lithology, tectonic setting, and state of stress. In this regard, we guess that such connections between seismicity and structural properties of fault systems are universal, being due to the action of feedback mechanisms and memory processes. References Ellsworth W. L.; 2013: Injection-induced earthquakes. Science, 341(6142), 1225942. Neely J. S., Salditch L., Spencer B. D. and Stein S.; 2023: A More Realistic Earthquake Probability Model Using Long‐Term Fault Memory. Bull. Seismol. Soc. Am., 113(2), 843-855. Rice J. R. and Cocco M.; 2007: Seismic fault rheology and earthquake dynamics. Tectonic faults: Agents of change on a dynamic earth, 99-137. Ross Z. E., Hauksson E. and Ben-Zion, Y.; 2017: Abundant off-fault seismicity and orthogonal structures in the San Jacinto fault zone. Sci. Adv., 3(3), e1601946. Tan Y. J., Waldhauser F., Ellsworth W. L., Zhang M., Zhu W., Michele M., ... and Segou M.; 2021: Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016–2017 Central Italy sequence. The Seismic Record, 1(1), 11-19. Zaccagnino, D., & Doglioni, C. (2022). The impact of faulting complexity and type on earthquake rupture dynamics. Communications Earth & Environment, 3(1), 258.