SWARM RECOGNITION ALGORITHM AS A PROMISING ARTIFICIAL INTELLIGENCE METHOD IN SEISMOLOGICAL RESEARCH
СЕЙСМОЛОГИЯЛЫҚ ЗЕРТТЕУЛЕРДЕГІ ЖАСАНДЫ ИНТЕЛЛЕКТІНІҢ ПЕРСПЕКТИВАЛЫ ӘДІСІ РЕТІНДЕ ҮЙІРЛІК ТАНУ АЛГОРИТМІ
АЛГОРИТМ РАСПОЗНАВАНИЯ РОЕВ КАК ПЕРСПЕКТИВНЫЙ МЕТОД ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В СЕЙСМОЛОГИЧЕСКИХ ИССЛЕДОВАНИЯХ
Abdullaev A.U. Litovchenko I.N. Lyutikova V.S.
November-December 2025National Academy of Sciences of the Republic of Kazakhstan
News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences
2025#2025-November-DecemberIssue 68 - 21 pp.
The article examines the current stage of seismic activity in the Northern Tien Shan region and adjacent territories in order to assess seismic hazard. Currently, there is another stage of seismic activity in the region, which necessitates the search for and application of new modern research methods and tools. The article presents an up-to-date method of pattern recognition as a promising approach to studying this problem. This is one of the applications of Artificial Intelligence (AI) in seismic research (related to assessing seismic hazard) based on recognizing the swarm activity of earthquakes in the region’s seismicity. The paper considers the current stage of seismic activity in the Northern Tien Shan region and adjacent territories, focusing on the recognition of earthquake swarms (weak seismicity) and their thermodynamic parameters. The paper highlights the spatial and temporal distributions of earthquake swarms for the period 2017-2025 and by year. The paper also describes the physical and mathematical criteria for recognizing earthquake swarms and the thermodynamic conditions in their sources. The calculation of thermodynamic conditions in the foci of earthquake swarms was carried out using a computational method that contains universal equations for calculating these parameters based on the magnitudes and energies of the occurring earthquakes. These studies will help to clarify the seismotectonic potential of the selected areas where earthquake swarms are concentrated, as well as provide a more accurate assessment of the seismic safety of the study area. The results obtained during the research and calculations play an important role in assessing the risks of severe earthquakes in the study area and potentially predicting their occurrence.
algorithm , Artificial Intelligence (AI) , earthquake swarm , pattern recognition , physical and mathematical criteria , seismic hazard assessment , seismicity , thermodynamic parameters
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National Scientific Center for Seismological Observations and Research of the Ministry of Emergency, Situations of the Republic of Kazakhstan, Almaty, Kazakhstan
National Scientific Center for Seismological Observations and Research of the Ministry of Emergency
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