## 5 Conclusions
The space-and-time model T(i) \(\times\) FCAZ for recognition of the areas prone to the strongest, strong, and significant earthquakes makes it possible to develop a schedule of subsequent iterations for the recognition of high seismicity hazard zones for the regions studied in this paper. The following regions with varying seismicity levels were studied by Barrier-3 and FCAZ methods in this paper:  
* Barrier-3--the Altai-Sayan-Baikal region (\(M\geq 6.0\)) and the Caucasus (\(M\geq 6.0\)). * FCAZ--mountain belt of the South American Andes (\(M\geq 7.75\)), the Pacific Coast of the Kamchatka Peninsula (\(M\geq 7.75\)), and the Kuril Islands (\(M\geq 7.75\)); California (\(M\geq 6.5\)); the Baikal-Transbaikal region (\(M\geq 5.5\), \(M\geq 5.75\), \(M\geq 6.0\)); the Altai-Sayan region (\(M\geq 5.5\)); the Caucasus (\(M\geq 5.0\)); the Crimean Peninsula and northwestern Caucasus (\(M\geq 4.5\), \(M\geq 5.0\)). The Altai-Sayan-Baikal region, the Pacific Coast of the Kuril Islands, and the Crimean Peninsula were first studied with the employment of methods for the recognition of earthquake-prone areas. Moreover, the Baikal-Transbaikal region was used as an example of the first recognition of earthquake-prone areas for the finite succession of growing magnitude thresholds \(M_{0}^{1}<M_{0}^{2}<M_{0}^{3}\). The joint presentation of the recognition results obtained by the Barrier-3 and Cora-3 algorithms in the Caucasus based on their composition with a fuzzy set allowed halving the number of missed targets. It was shown, using California and the Pacific Coast of the Kamchatka Peninsula as an example, that the existence of foreshock and aftershock sequences in the catalogs of earthquakes does not have a significant impact on the FCAZ recognition results. A totality of control experiments conducted in this paper demonstrates the reliability and reproducibility of the interpretation of FCAZ zones as the areas prone to the strongest, strong, and significant earthquakes. In the studied regions, FCAZ zones occupy a relatively small area as compared with the total seismicity field, which makes up 30%-40% of the total seismicity space and 50%-65% of the space where earthquakes with \(M\geq M_{\rm R}\) occur. This illustrates the spatial nontriviality of the obtained results. Findings from the paper also demonstrate that low seismicity can actually \"manifest\" the properties of geophysical fields, which in the classical EPA approach are used directly as the characteristics of recognition objects. Conceptualization, B.A.D. and A.D.G. ; data curation, B.A.D. ; formal analysis, B.A.D., A.D.G., and S.M.A. ; funding acquisition, B.A.D. ; investigation, B.A.D. and A.D.G. ; methodology, B.A.D., A.D.G., and S.M.A. ; project administration, B.A.D. ; resources, J.K.K. and B.V.D. ; software, B.A.D. and I.O.B. ; validation, B.A.D., A.D.G., J.K.K., and B.V.D. ; visualization, B.A.D. and Y.V.B. ; writing--original draft, B.A.D., A.D.G., B.V.D., and Y.V.B. All authors have read and agreed to the published version of the manuscript. The reported study was funded by RFBR, project number 20-35-70054 +Systems approach to recognition algorithms for seismic hazard assessment. This work employed data provided by the Shared Research Facility \(*\)Analytical Geomagnetic Data Center>> of the Geophysical Center of RAS ([[http://ckp.gcras.ru/](http://ckp.gcras.ru/)]([http://ckp.gcras.ru/](http://ckp.gcras.ru/)) access date: 30 July 2021). The authors declare no conflict of interest.