The fuzzy clustering and zoning method (FCAZm) of systems analysis is suggested for recognizing the areas of the probable generation of the epicenters of significant, strong, and the strongest earthquakes. FCAZm is a modified version of the previous FCAZ algorithmic system, which is advanced by the creation of the blocks of artificial intelligence that develop the system-forming algorithms. FCAZm has been applied for recognizing areas where the epicenters of the strongest ( ≥ 7/4) earthquakes within the Andes mountain belt in the South America and significant earthquakes ( ≥ 5) in the Caucasus can emerge. The reliability of the obtained results was assessed by the seismic-history type control experiments. The recognized highly seismic zones were compared with the ones previously recognized by the EPA method and by the initial version of the FCAZ system. The modified FCAZm system enabled us to pass from simple pattern recognition in the problem of recognizing the locations of the probable emergence of strong earthquakes to systems analysis. In particular, using FCAZm we managed to uniquely recognize a subsystem of highly seismically active zones from the nonempty complement using the exact boundary.

Microseismic sounding along the profile in the Ossetian sector of the Greater Caucasus revealed two domains with characteristic properties and morphology deep beneath the mountain system. One...

Clustering the epicenters of Caucasian earthquakes with magnitudes M ≥ 3.0 is carried out, and the epicentral zones of the probable earthquakes with M ≥ 5.0 areas where epicenters of earthquakes with M ≤ 5.0 may occur are recognized by the Fuzzy Clustering and Zoning (FCAZ) algorithmic system developed by the authors at the Geophysical Center of the Russian Academy of Sciences. These zones correspond well to the locations of the epicenters of earthquakes with M ≥ 5.0. The zones recognized in this study are compared with the zones previously recognized by A.D. Gvishiani et al. in 1988 by the Earthquake-Prone Areas Recognition (EPA) technique. The comparison shows that the zones identified by FCAZ are mainly located inside the EPA-zones. The FCAZ-zones are also compared with the zones previously recognized using gravimetric and geological data. The results obtained by different methods closely agree. Contrary to EPA technique FCAZ algorithmic system relies on the DPS algorithm of objective classification that requires only the information about epicenters of the earthquakes in the region under study.

The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The article describes the catalog compilation algorithm, as well as formalized procedures for removing duplicates and choosing the optimal magnitude scale. Due to different network configurations and record processing methods, different agencies may register/miss different events. This results in the absence of some events in different earthquake catalogs. Therefore, merging the data of various seismological agencies will provide the most complete catalog for the studied region. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) necessarily arises. An additional difficulty arises when distinguishing between aftershocks and duplicates since both are events that are close in space and time. To solve this problem, we used a modified nearest neighbor method developed earlier by the authors. The modified version, which is focused on identifying duplicates and distinguishing between duplicates and aftershocks, uses a probabilistic metric in the network error space to determine the epicenters and times of seismic events. In the present paper, a comparison and regression analysis of the different magnitude types of the integrated catalog is carried out, and based on the obtained ratios, the magnitude estimates are unified.

—The paper addresses almost half a century’s development of pattern recognition algorithms’ application for solving the problem of determination of strong earthquake-prone areas. This approach was named Earthquake-Prone Areas (EPA). The pattern recognition algorithms applied for this purpose, the studied regions, and the methods for assessing the reliability of the obtained results including the theory of dynamic and limit recognition problems are considered. A recently developed alternative method for solving the problem by identifying the clusters of earthquake epicenters is also presented. This method is based on the approaches of the Discrete Mathematical Analysis (DMA) and implemented in the form of the algorithmic system named Formalized Clustering and Zoning (FCAZ). The comparison of the results obtained by the EPA approach and FCAZ system shows their good consistency which provides an additional argument in favor of their reliability. The possibilities for further development and joint application of the EPA approach and FCAZ system and creating, on this basis, an integrated method for systems analysis with the inclusion of artificial intelligence are outlined. In case of success, this method is expected to be used in seismic hazard assessment and planning of earthquake-resistant construction.