## 3 Results  
### Variable EPA Method  
Figure 2 shows the MSZ map of Altai-Sayan-Baikal region. The result of earthquake-prone areas recognition with \(M\geq 6.0\) by Barrier-3 algorithm is shown in Figure 2 using ellipses with blue boundaries. Upon the completion of recognition, 32 out of 97 objects are identified as high seismicity class \(B\). The totality of vicinities of these objects (circles used to compute the values of characteristics) [5] determines the areas prone to earthquakes with \(M\geq 6.0\). Figure 3a shows a bar diagram characterizing the medium contribution of characteristics in recognition using the Barrier-3 algorithm of a sought high seismicity set of objects. Figure 3b shows the contribution of characteristics expressed through attribution to the Top 3 rankings. The \(y\) axis in Figure 3a shows the average number of \"attributions\" of characteristics in the recognition of a set \(P_{\rm TI}(B_{0})\) (see above), in Figure 3b, the number of attributions to the three \"strongest\" characteristics (Top 3 ranking). It can be seen from Figure 3 that in the recognition of strong earthquake-prone areas in the Altai-Sayan-Baikal region using the Barrier-3 algorithm, the most significant characteristics include gravity anomalies (Bmax and Bmin), a combination of relief types (Top) and the distance to the nearest lineament of rank II (R2). The intersections of lineaments classified as high seismicity ones against the background of the entire set of objects in their vicinities are characterized by low values of gravity anomalies (mostly Bmax \(\leq-160\) mGal and Bmin \(\leq-220\) mGal) and contrasting combinations of relief types--mountains/foothill and mountains/mountains. These are characterized by high values of magnetic anomaly (MOmax), the concentration of dB around 60 mGal and 120 mGal, and the concentration of Hmin less than \(-1000\) m and more than 1000 m.  
It can be seen from Figure 3 that lithospheric magnetic field anomalies contribute to the result of recognition in the Altai-Sayan-Baikal region. It can thus be concluded that the vicinities of high seismicity intersections of lineaments are characterized by a high degree  
Figure 3: The contribution of characteristics to recognition using the Barrier-3 algorithm of a high seismicity set of objects in the Altai–Sayan–Baikal region: (**a**) average contribution of characteristics; (**b**) the contribution of characteristics expressed through their attribution to the “strongest” threes. Figure 2: The morphobrstructural zoning map of the Altai-Sayan–Baikal region. Thick black lines—rank I lineaments; medium gray lines—rank II; thin black lines—rank III; solid lines—longitudinal lineaments; dotted line—transverse ones ([36; 37]), areas prone to earthquakes with \(M\geq 6.0\) (ellipses with blue boundaries—Barrier-3 ([16]), white ellipses—Cora-3 ([37]), white ellipses with blue boundaries—both the algorithms). Red circles designate the epicenters of crustal earthquakes with \(M\geq 6.0\) (1900–2012) used to form the learning set \(B_{0}\), red star refers to the epicenter of crustal earthquake (11 January 2021 with \(M=6.7\)) that occurred after the completion of recognition. of tectonic breaks, the existing deep density heterogeneity, as well as specific structure and composition of the Earth's crust. It would be natural to interpret these signs as the criteria of high seismicity in the studied region. A comparative analysis of results, obtained independently using the Barrier-3 algorithm and the Cora-3 dichotomy algorithm, which is most common in the EPA [38; 39], shows that they are well aligned with each other (Figure 2). The Barrier-3 algorithm recognized as high seismicity ones 32 intersections of morphostructural lineaments and Cora-3 33 intersections [36; 37]. That said, 25 objects were attributed by both algorithms to class \(B\). The Barrier-3 algorithm classified as hazardous 6 out of 51 objects of the learning set of a low seismicity class of dichotomy; Cora-3 3 out of 51; both algorithms, 2 intersections.