## 1 Introduction
This method is trained on synthetically generated waveforms that simulate real P waves, allowing it to estimate P-wave arrival times [27] accurately. One of the key advantages of Dpick is its ability to achieve high detection performance without requiring large amounts of training data, making it a practical solution for real-time seismic monitoring. Deep learning has demonstrated significant potential in the field of earthquake prediction. Models such as convolutional neural networks have proven to be highly effective in various tasks, including signal processing [28]. These models can automatically extract critical features from the data, making them particularly useful for analyzing seismic signals [29]. Research on local and regional seismic activity is crucial in understanding earthquake dynamics and improving detection accuracy. In [30], an analysis of seismic events in Southern California using data from the Southern California Earthquake Data Center (SCEDC) identified patterns in magnitude distribution and earthquake frequency. These findingscontribute to a better understanding of regional seismic behavior and its implications for assessing earthquake hazards. In [31], calibration methods for seismic stations were presented to enhance the accuracy of amplitude measurements, which is essential to determine the magnitude of the earthquake. In [3], convolutional neural networks were also trained to estimate the magnitude and frequency of the earthquake directly from seismograms, eliminating manual feature extraction. Furthermore, a CNN-based model was applied to determine the arrival times of the P-wave and the polarity of the first motion using 19.4 million seismograms. This model selected the arrival time in 0.028 s from the picks of the human analyst in 75% of the cases, while the first motion polarity classification reached an accuracy of 95%. The use of neural networks for P-wave detection and the development of earthquake early warning systems represent promising research directions. These approaches improve the accuracy and speed of seismic event detection and open new possibilities for integrating advanced technologies into existing monitoring frameworks. However, the challenges of earthquake detection and early warning remain unresolved despite recent advances and significant research efforts. Various factors contribute to this complexity, making the development and implementation of an automated P-wave detection system a highly relevant task. One of the important limitations of the existing methods is their insufficient consideration of geographical structures, surface composition, and terrain characteristics. These factors can significantly influence seismic wave propagation and detection accuracy. Therefore, the development of neural networks tailored to specific local regions is essential to improve the reliability of earthquake early warning systems. Such region-specific models can better account for local geological variations, leading to more effective seismic monitoring and risk mitigation strategies. In this study, we utilized convolutional neural networks for P-wave detection in the Almaty region. This region was chosen for geophysical reasons, including its high level of seismic activity. Almaty is the most seismically active zone in the Republic of Kazakhstan, situated on 27 tectonic plates. The city has experienced two devastating earthquakes in the past, making it highly likely that they will recur. Studies of this area and the regions surrounding Almaty have not been conducted sufficiently.