Articolo in rivista, 2023, ENG, 10.1016/j.chaos.2023.113419
Zaccagnino Davide; Telesca Luciano; Doglioni Carlo
a. Earth Science Department, Sapienza University, Piazzale Aldo Moro, 5, Rome, 00185, Italy b. Institute of Methodologies for Environmental Analysis, National Research Council, Zona Industriale C.P. 27, Tito Scalo, 85050, Italy c. National Institute of Geophysics and Volcanology, Via di Vigna Murata, 605, Rome, 00143, Italy
The estimation of the maximum expected magnitude is crucial for seismic hazard assessment. It is usually inferred via Bayesian analysis; alternatively, the size of the largest possible event can be roughly obtained from the extent of the seismogenic source and the depth of the brittle-ductile transition. However, the effectiveness of the first approach is strongly limited by catalog completeness and the intensity of recorded seismicity, so that it can be of practical use only for aftershocks, while the second is affected by extremely large uncertainties. In this article, we investigate whether it may be possible to assess the magnitude of the largest event using some statistical properties of seismic activity. Our analysis shows that, while local features are not appropriate for modeling the emergence of peaks of seismicity, some global properties (e.g., the global coefficient of variation of interevent times and the fractal dimension of epicenters) seem correlated with the largest magnitude. Unlike several scientific articles suggest, the b-value of the Gutenberg-Richter law is not observed to have a predictive power in this case, which can be explained in the light of heterogeneous tectonic settings hosting fault systems with different extension.
Chaos, solitons and fractals 170 , pp. Art.n.103419-1–Art.n.103419-8
Seismogenic potential, Seismic clustering, Maximum expected magnitude, Clustering coefficients, Fractal dimension, b-value
ID: 487455
Year: 2023
Type: Articolo in rivista
Creation: 2023-10-16 11:33:12.000
Last update: 2023-10-16 11:33:12.000
CNR authors
CNR institutes
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.chaos.2023.113419
URL: https://www.sciencedirect.com/science/article/pii/S096007792300320X
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:487455
DOI: 10.1016/j.chaos.2023.113419
ISI Web of Science (WOS): 001030066300001
Scopus: 2-s2.0-85151240441