Bachelor and master's degrees at Farabi Kazakh National University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Science, Electrical and Electronic Engineering
4
Scopus Publications
Scopus Publications
Detection of P-Wave Arrival as a Structural Transition in Seismic Signals: An Approach Based on SVD Entropy Margulan Ibraimov, Zhanseit Tuimebayev, Alua Maksutova, Alisher Skabylov, Dauren Zhexebay, et al. Smart Cities, 2026 Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels (SNR≥2). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at SNR≥2, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems.
The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis Alisher Skabylov, Aldiyar Agishev, Dauren Zhexebay, Margulan Ibraimov, Serik Khokhlov, et al. Applied Sciences Switzerland, 2025 This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications.
COMPARATIVE ANALYSIS OF STATE-OF-THE-ART NEURAL NETWORKS FOR ART OBJECT RECOGNITION Y. Kozhagulov, A. Maksutova, D. Zhexebay, A. Skabylov, T. Kozhagulov Herald of the Kazakh British Technical University, 2025 Currently, information technology is rapidly developing and one of its branches can be called machine translation. The use of machine translation in the process of understanding each other by people from different countries is increasing every year. At the moment, Google and Yandex machine translations are among the best machine translations. The quality of machine translation from Yandex and Google is improving every year. However, according to the results of the experiment, when translating from English or Russian into Kazakh and Turkic languages, the quality of the translation decreases. This was shown by the translation result obtained from these two machine translations in March 2024. After all, translation has also shown that it is directly related to the structure of language. Since 2000, scientists from the state of Kazakhstan have been actively studying translations into the Kazakh language. The goal of the work is to improve the quality of translation from English into Kazakh. For this purpose, a transforming model was created for the Kazakh and Turkic languages for learning translation in neural machine translation OpenNMT(). The created model studied and learned an English-Kazakh parallel corpus of 180,000 words. Later, the document with a structure of 20,000 different English sentences was translated into Kazakh. The result is measured using the Blue() metric. The translation result showed a high level. It is shown that in order to improve the results of the experiment carried out in the work during model training, it is necessary to increase the number of parallel corpora created from the English-Kazakh language pair.
