Abstract
Archaeoseismology can provide a useful chronological tool for constraining earthquakes and documenting significant evidence that would otherwise be lost. In this paper, we report a case of surface faulting on ancient man-made structures belonging to the archaeological site of Santa Venera al Pozzo situated along the eastern flank of Mt. Etna volcano in eastern Sicily (southern Italy), which is affected by well-developed tectonic faults. Geological surveys highlight a set of fractures affecting the archaeological ruins, suggesting the occurrence of a capable fault zone across the area. An integrated geophysical survey was carried out in order to identify the main subsurface tectonic discontinuity ascribable to the fault zone. The information derived from different geophysical techniques, such as electrical resistivity tomography, seismic refraction tomography, ground-penetrating radar, and magnetic surveys allowed us to infer that the fractures observed at the surface could have been produced by coseismic rupture. They are conceivably linked to a strong earthquake that probably occurred in the Roman period, around mid-end of the third-century AD; time constraints are inferred through the dating of buildings of the archaeological site.
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Acknowledgements
We wish to thank Maria Costanza Lentini and Donatella Aprile, Executive Director and Manager of the Polo Regionale of Catania, respectively, who allowed us to perform the geophysical investigations. Special thanks go to Susanna Amari, archaeologist Soprintendenza BB.CC.AA. di Catania who worked for a long time in site of Santa Venera al Pozzo, for useful information on historical and archaeological data. We also thank the referee Luigi Ferranti (University of Napoli-Federico II), and an anonymous one, for their constructive comments and suggestions that greatly improved the paper.
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The cluster analysis is a procedure that allows the identification, within a set of objects, of some subsets (called clusters) that tend to be homogeneous within them. The statistical units are divided into a number of groups according to their level of similarity, and evaluated from the values that a number of chosen variables take in each unit. Generally, in the analysis for grouping it is not necessary to have in mind any interpretative model (Fabbris 1983). The techniques of cluster analysis create groups so that each observation is very similar to all the others that belong to the same group, according to some criteria. At the end of the procedure, the final clusters should exhibit a high internal consistency (intra-cluster) and high external heterogeneity (inter-cluster). So, if the partition is successful, the objects within the clusters are close to each other, while objects belonging to different clusters are more distant from each other (Barbarito 1999). In the centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set. When the number of clusters is fixed to k, the clustering can be formally regarded as an optimization problem:
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find the k cluster centres and assign each object to the cluster, such that the squared distances from the cluster centroid are minimized;
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calculate the new means to be the centroids of the observations in the new clusters.
The algorithm converges to a (local) optimum when the assignments no longer change. There is no guarantee that the global optimum is found using this algorithm.
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Bottari, C., Martorana, R., Scudero, S. et al. Coseismic Damage at an Archaeological Site in Sicily, Italy: Evidence of Roman Age Earthquake Surface Faulting. Surv Geophys 39, 1263–1284 (2018). https://doi.org/10.1007/s10712-018-9482-2
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DOI: https://doi.org/10.1007/s10712-018-9482-2