# Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition
## 4 Discussion
At the same time, we emphasize that the epicenter of the earthquake on 6 July 2019 with \(M=7.1\) (white star in Figure 11) [61], being inside the FCAZ zones, was located outside the areas determined for the magnitude threshold \(M=6.5\) being high seismicity under the EPA method [21]. The latter once again emphasizes the modernity and reliability of the obtained FCAZ results. The result of applying the new computational component Int in the FCAZ structure is shown in Figure 11 in blue against the background of red zones mapped by the Ext algorithm. Thus, we have a new shell \(\mathrm{Ext(DPS)}\cup(\mathrm{Int(DPS)}\setminus\mathrm{Ext(DPS)})\) of high-seismicity DPS clusters in California, which we will consider in this paper as new FCAZ zones prone to strong earthquakes. The new FCAZ zones had the same number of \"miss target\" errors. As before, five earthquake epicenters were outside the recognized zones. However, it should be noted that the development of the FCAZ algorithmic basis, including the expansion of the algorithmic base of the method, could not directly lead to a decrease in the number of target misses. The contribution of the updated mathematical toolkit of the method which significantly develops FCAZ-seismic zoning is as follows. At the second (described in detail above) stage of the application of the DPS algorithm in California, clusters were formed that were \(r\)-connected components (Equation (4)). According to the mathematical construction embedded in the third DPS stage, some of these connected components need to be combined into large clusters that have a close internal connection against the background of all  
Figure 11: New FCAZ zones prone to earthquakes with \(M\geq 6.5\) in California and earthquake epicenters with \(M\geq 6.5\). recognized dense condensations. However, the Ext algorithm does not always allow building a single FCAZ zone for clusters that are united at the third DPS stage. The Int algorithm effectively solves this problem. From Figure 11, it can be seen that the result of the operation of the Int algorithm made it possible to combine several zones mapped by the Ext algorithm into individual high-seismicity areas. This was especially well observed in the south and east of the Sierra Nevada mountains, in the ocean area south of Los Angeles, as well as northeast of the city of San Diego. In the last location, Int made it possible to connect the edges of a large FCAZ zone mapped by the Ext algorithm. Thereby, the degree of falling into this zone of two strong earthquake epicenters increased. Before the application of the Int algorithm, these epicenters were located on the very edge of the recognized FCAZ zones. From Figure 11, it can be concluded that the FCAZ recognition result obtained in California using the updated algorithmic base, which presented more interconnected potential high-seismicity zones, has a greater degree of reliability. Thus, based on the developed mathematical apparatus, the reliability and accuracy of the FCAZ recognition of areas prone to earthquakes were significantly increased. This in turn contributes to the prediction of damage from earthquakes and, for the first time, can be directly used to update seismic zoning maps.