# Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition
## 4 Discussion  
As mentioned above, the FCAZ method in its initial form, which has been used to date to recognize the areas prone to the strongest, strong, and significant earthquakes in a number of mountainous countries of the world [26], is a sequential application of the first stage of the SDPS algorithm (in the papers devoted to FCAZ recognition, it is just the DPS algorithm) and the Ext algorithm, where \(\text{FCAZ}(X)=\text{Ext}(\text{DPS}(X))\)[28]. In the present work, the algorithmic base of the FCAZ method was substantially developed. Thus, in particular, its algorithmic base was expanded. Using California as an example, let us consider the contribution of the Int algorithm developed in this paper to the mapping of potential high-seismicity zones (i.e., contouring of the recognized DPS clusters of epicenters of weak earthquakes). In Figure 11, the green and red colors show the results of FCAZ recognition of strong earthquake-prone areas with magnitudes \(M\geq 6.5\) in California from [26]. Earthquake epicenters from the ANSS catalog ([[http://www.ncedc.org/anss/catalog-search.html](http://www.ncedc.org/anss/catalog-search.html)]([http://www.ncedc.org/anss/catalog-search.html](http://www.ncedc.org/anss/catalog-search.html)), accessed on 1 January 2020) with \(M\geq 3.0\) that occurred over the period of 1960-2012 were used as recognition objects (DPS clustering). The choice of the magnitude threshold \(M=3.0\) was carried out based on theoretical and practical analysis of the magnitude-frequency graphs for the entire considered region. The green color in Figure 11 shows the DPS clusters, while red shows the high-seismicity zones mapped by the Ext algorithm based on the clusters within which earthquakes with \(M\geq 6.5\) can occur. In [26], the arguments in favor of the reliability of the performed recognition are presented in detail. In turn, it was shown in [50] that the result of FCAZ recognition in California depended little on the presence or absence of foreshock and aftershock sequences in the earthquake catalog, which is the source of recognition objects. The black stars in Figure 11 show the epicenters of 33 earthquakes with \(M\geq 6.5\) that occurred over the period of 1836-2010. The blue and white stars show the epicenters of events with \(M\geq 6.5\) that occurred in 2014 and 2019 (i.e., after the end of the instrumental catalog, which made up a set of recognition objects). These events formed the material of a pure experiment. Thus, the consistency of the recognized FCAZ zones with the locations  
Figure 10: FCAZ zones constructed for the results from running the first stage of the SDPS algorithm on the array X (Figure 6b). (**a**) Extzones. (**b**) Int zones. (**c**) Int against the background of Ext. (**d**) Ext on the background of Int. of the epicenters of strong earthquakes that occurred more than 180 years ago, including the ones before and after the beginning of the instrumental catalog used in recognition, was checked. Out of 35 events with \(M\geq 6.5\), only 5 (14.3%) epicenters did not fall into the recognized FCAZ-zones (Figure 11). At the same time, it should be noted that three epicenters located in the northwest of the region are located in the Pacific Ocean at a fairly large distance from the coast. Additionally, two epicenters of historical earthquakes did not fall into the FCAZ zones: near Fort Tehon (1857) and in San Francisco (1906). These earthquakes occurred long before the start of systematic instrumental observations of seismicity in California. Thus, for various reasons, there were not enough objects (earthquake epicenters with \(M\geq 3.0\)) to recognize the areas of these five strong earthquakes. The positive result of the pure experiment should be noted: both epicenters fell strictly inside the FCAZ zones (Figure 11).