Alexander I. Gorshkov

Sakhalin Island is located at the boundary of the Okhotsk and Eurasian plates, the interaction of which governs Sakhalin’s seismicity. The aim of this study is to identify potential earthquake sites with M_w ≥ 6.0. This problem is solved using morphostructural zoning (MSZ) data processed by the Kora-3 pattern recognition algorithm, combined with geomorphological and geophysical data. The MSZ revealed the lineament–block structure of the island and determined the positions of morphostructural nodes that host the epicenters of M_w ≥ 6.0 earthquakes. The study objects were 56 nodes whose positions were established by MSZ. The training set of the recognition algorithm consisted of 8 nodes with known M_w ≥ 6.0 earthquakes. As a result, in addition to the nodes that have already experienced M_w ≥ 6.0 events, three nodes located in northern Sakhalin were identified as dangerous (seismically hazardous). In central and southern Sakhalin, in addition to the nodes where M_w ≥ 6.0 earthquakes have already occurred, no other potential nodes were identified. The characteristic geomorphological and geophysical features of the recognized dangerous nodes are determined.

The aim of this article is to test the fractal dimension of relief as a new parameter for recognizing ore-bearing morphostructural nodes in the Caucasus and Altai–Sayan regions. Polymetallic deposits of various sizes known in these regions were considered. The objects of recognition are morphostructural nodes, the positions of which were determined in previous studies using the method of morphostructural zoning, where it was shown that the spatial localization of polymetallic deposits in these regions is controlled by morphostructural nodes. For each node, the fractal dimension (FD) was calculated from SRTM digital elevation models using the box-counting method. The informativeness of FD was assessed a priori using a discretization procedure, which made it possible to determine characteristic FD value intervals for nodes hosting known polymetallic deposits and for nodes where such deposits are unknown. Together with morphometric and geological–geophysical parameters of the nodes, the FD parameter was used to recognize ore-bearing nodes using the Kora-3 pattern recognition algorithm. The obtained results were compared with the classification of nodes into ore-bearing (class O) and non-ore-bearing (class NO) that had been previously identified in the study regions without using the FD parameter. It is shown that the fractal dimension of relief is an informative parameter for recognizing potentially ore-bearing morphostructural nodes in the regions considered.

The Altai–Sayan mountain-folded belt is analyzed with the purpose of (1) revealing peculiarities of localization of large-scale polymetallic mineralization in the lineament-block structure of the region and (2) determining the geophysical and geomorphic peculiarities of the locations of these deposits using the Cora-3 pattern recognition algorithm. The lineament-block structure of the region is determined using morphostructural zoning. A spatial correlation between large and superlarge polymetallic deposits and morphostructural nodes is revealed. Based on this correlation, a dichotomy problem is solved, which is to divide the entire set of nodes in the region into two classes—ore-bearing and non-ore bearing. For this purpose, we used the Cora-3 logical recognition algorithm with training, for which the input data are geomorphological and geophysical parameters of the nodes. The training set of the algorithm was composed of the nodes where large and superlarge polymetal deposits are known. At the training stage, the algorithm identified the sets of the characteristic features that are peculiar to each class. Based on these features, all the nodes in the region were divided into ore-bearing and non-ore-bearing ones. As a result of recognition, the nodes in which deposits of the considered types and sizes are known were classified as ore-bearing, and, in addition to them, another 11 nodes were identified that meet the features determined in the work and can be considered potentially ore-bearing.

—The Altai–Sayan mountain-folded belt is analyzed with the purpose of (1) revealing peculiarities of localization of large-scale polymetallic mineralization in the lineament-block structure of the region and (2) determining the geophysical and geomorphic peculiarities of the locations of these deposits using the Cora-3 pattern recognition algorithm. The lineament-block structure of the region is determined using morphostructural zoning. A spatial correlation between large and superlarge polymetallic deposits and morphostructural nodes is revealed. Based on this correlation, a dichotomy problem is solved, which is to divide the entire set of nodes in the region into two classes—ore-bearing and non-ore bearing. For this purpose, we used the Cora-3 logical recognition algorithm with training, for which the input data are geomorphological and geophysical parameters of the nodes. The training set of the algorithm was composed of the nodes where large and superlarge polymetal deposits are known. At the training stage, the algorithm identified the sets of the characteristic features that are peculiar to each class. Based on these features, all the nodes in the region were divided into ore-bearing and non-ore-bearing ones. As a result of recognition, the nodes in which deposits of the considered types and sizes are known were classified as ore-bearing, and, in addition to them, another 11 nodes were identified that meet the features determined in the work and can be considered potentially ore-bearing.

On February 6, 2023, two devastating earthquakes struck southern central Turkey, nin hours apart. The ground shaking from these earthquakes even swept over a significant part of northwestern Syria. In this paper, we consider the locations of the epicenters of these earthquake relative to the prediction of М ≥ 6.5 earthquake epicenters in Anatolia and adjacent regions, which was made in 1973 by I.M. Gelfand, V.I. Keilis-Borok and their colleagues using the Kora-3 pattern recognition algorithm based on morphostructural zoning data. We also present the results of early detection of periods with an increased probability of the strongest earthquakes as determined by the medium-term prediction algorithm for M8 earthquakes. It is found that the epicenters of the February 6, 2023 earthquakes occurred in a knot identified in 1973 as potentially earthquake-prone for М ≥ 6.5, and both events occurred within the spatial and temporal boundaries of the alert area diagnosed by the M8 algorithm in July 2021.

—On February 6, 2023, two devastating earthquakes struck southern central Turkey, nine hours apart. The ground shaking from these earthquakes even swept over a significant part of northwestern Syria. In this paper, we consider the locations of the epicenters of these earthquakes against the prediction of the М ≥ 6.5 earthquake epicenters in Anatolia and adjacent regions, which was made in 1973 by I.M. Gelfand, V.I. Keilis-Borok and their colleagues using the Kora-3 pattern recognition algorithm based on morphostructural zoning data. We also present the results of early detection of the periods with an increased probability of the strongest earthquakes as determined by the medium-term prediction algorithm for the M8 earthquakes. It is found that the epicenters of the February 6, 2023 earthquakes occurred in a node identified in 1973 as potentially earthquake-prone for М ≥ 6.5, and both events occurred within the spatial and temporal coverage of the warning area diagnosed by the M8 algorithm in July 2021.

Information about potential earthquake sources is a key issue for seismic hazard assessment. This study presents the application of a phenomenological approach based on pattern recognition to determine the possible locations of strong earthquakes in the Bulgarian region. The technique assumes the origin of strong earthquakes in morphostructural nodes formed around the intersections of morphostructural lineaments identified by morphostructural zoning. For the territory of the Bulgaria and neighbouring regions, 178 nodes were defined in this work. The CORA-3 pattern recognition algorithm identified 59 seismogenic nodes analysing a set of geophysical and geological node’s characteristics. The identified seismogenic nodes are capable to generate earthquakes with magnitude equal to or greater than 6 and are located at the boundaries between the largest tectonic domains: Rila, Pirin, and Rhodope orogens; the Serbian-Macedonian massif; and in the Stara Planina belt. The set of characteristic features of seismogenic nodes indicates that the vicinity of potential nodes is characterized by a high contrast of neotectonic movements of the Earth’s crust and the presence of deep heterogeneities in the Earth’s crust. About 40% of the recognized nodes are not associated with any earthquakes, while the rest of the recognized seismogenic nodes are characterized by an area with a radius of 25 km where earthquakes are known to occur. Part of these “non active” seismogenic nodes are close to the historical events with magnitudes higher than 5.5 since the magnitude and location of historical events have large uncertainties. Another part of the seismogenic nodes may slightly change the location due to the uncertainties in morphostructural zonation. Other nodes may indicate unknown historical seismicity or paleoearthquakes. Defined M6+ seismogenic nodes can fill the potential gaps in the recorded seismicity on the territory of Bulgaria, thus to improve the seismic hazard assessment of the studied region.

Seismogenic nodes, able to locate earthquakes with magnitudes M equal or higher than 6 (M6+), are identified for the territory of Bulgaria and adjacent areas. Definition of nodes is based on morphostructural zonation. Pattern recognition algorithm Cora-3 is applied to identify the seismogenic nodes, characterized by specific geological, geophysical and morphological data. The pattern recognition algorithm is trained on information for 30 seismic events M6+ for the period 29 B.C. – 2020, selected from historical and instrumental Bulgarian earthquake catalogues. These events are associated with 16 "training" nodes. Totally we have recognized 56 seismogenic nodes, most of them in southwestern Bulgaria.The analysis of the identified seismic nodes shows that in addition to the initial 16 "training" nodes, about 20 ones may be associated with the already observed seismicity. Some of these nodes may be related with documented seismicity, which is not taken into account to select the “training” nodes. Other seimogenic nodes are close to (but do not include) some historical earthquakes, whose location or magnitude is not precise enough.There has not been registered seismic activity M6+ in the vicinity of other 20 seismogenic nodes. To consider a certain inaccuracy in the determination of the earthquake magnitudes, in the analysis of these seismogenic nodes we take into account earthquakes with M higher than 5.8. In such a way the number of “inactive” until now seismogenic nodes decrease further. Several nodes can be related to seismic activity in the past, established by geological research. But a significant part of seismic nodes remains, which cannot be associated with any seismic manifestations. This is a sign of possible future earthquakes M6+ near these nodes.It should be noted that 3 of the earthquakes M6+ in the twentieth century were not close to any seismogenic node. This may be due to several reasons among which are: hidden tectonic structures, gaps in the morphostructural zonation or an inaccurate magnitude of the earthquake’s catalogue data.Acknowledgements. This study is partly funded by Russian Foundation of Basic Research (RFBR) according to the research projects 20-55-18008 and by Bulgarian National Science Fund, research project KP-06-Russia-29/16.12.2020.