Tectonic Regimes Inferred From Clustering of Focal Mechanisms and Their Distribution in Space: Application to the Central Mediterranean Area. Issue 1 (6th January 2021)

• Record Type: Journal Article
• Authors: Scarfì, L.; Langer, H.; Messina, A.; Musumeci, C.
• Is Part Of: Journal of geophysical research. Volume 126:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-06
• Subjects: 3D gridding -- focal mechanisms -- unsupervised learning techniques -- tectonic regimes; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2020JB020519
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: The study of the kinematics and stress field related to seismicity makes an important contribution to the understanding of tectonic processes. In this kind of analysis, a crucial issue is identifying seismically homogeneous areas, which implies data classification and cluster creation. We present an approach that combines unsupervised learning techniques in order to reveal patterns in the focal mechanisms data set. In particular, a combination of two popular clustering algorithms, that is, self‐organizing maps and Fuzzy C‐means, was applied to focal mechanisms of events located in the Central Mediterranean region, characterized by a complex geodynamic framework. The analysis allowed identifying eight groups of focal mechanisms and their spatial distribution in the crust, and revealing the tectonic style of key sectors of southern Italy and of the neighboring offshore areas. A compressive regime was found between the lower Tyrrhenian Sea and southeastern Sicily, whereas extension prevails along the Calabrian Arc and the southern Apennines. A NW‐SE transcurrent faulting between the Aeolian Islands and the Ionian Sea forms a transfer zone between these two domains. Plain Language Summary: The study of mechanisms describing the movement of the crustal blocks in a fault and of the forces causing earthquakes, provides precious information on the ongoing tectonic processes in a region. An important aspect in such an analysis is distinguishing the type of fault mechanism and identifying seismically homogeneous areas. To this end, in this work we have used a two‐step approach that combines unsupervised learning—where objects are classified by the computer on the basis of their characteristics—and an analysis of the spatial distribution of the objects. This approach has been applied to a data set selected from the earthquakes recorded in the past 40 years in the Central Mediterranean region. The first step allowed distinguishing the earthquake mechanisms into fairly homogeneous groups; the final step was the analysis of their distribution in space, aiming at identifying prevailing regimes of deformation within the crust. In this way, we were able to characterize the different tectonic processes acting in key areas of the studied region. Key Points: Underlying patterns in focal mechanisms can be revealed by an approach that combines unsupervised learning techniques The prevailing kinematics and stress regime are inferred by a spatial analysis and focal mechanisms categorization The tectonic regime characterizing key sectors of the studied region is identified