## 4 Conclusions  
This study developed and trained a convolutional neural network for the automatic detection of P waves in seismic data. Experimental results demonstrated that the proposed method achieved a P-wave detection accuracy of 94.1% and a recall of 89.1%, making it comparable to some of the best deep learning-based approaches currently available.  
The model used seismic data from the IRIS database for training and testing, collected from seven seismic stations within a 333 km radius of Almaty. The Almaty region is one of the most seismically active areas in the country, making the dataset highly unique and valuable for this region. The complex terrain and diverse geological characteristics introduce additional challenges for seismic wave detection, further emphasizing the significance of the proposed approach.  
One of the key advantages of the developed model is its high accuracy and reliability in P-wave detection, which is critical for precise and timely seismic activity analysis. Using convolutional neural networks enables the automation of large-scale seismic data analysis, eliminating the need for manual annotation of specific regions and significantly accelerating the detection process. This is one of the developed model's key advantages.  
Furthermore, the method has shown a high degree of adaptability to local geophysical characteristics, allowing for effective monitoring of seismic activity in complex and geologically diverse regions.  
Conceptualization, D.Z., A.A. ([PERSON]) and A.S.; methodology, M.I.; software, D.Z., G.K. and A.O.; validation, G.K., A.O. and A.A. ([PERSON]); formal analysis, S.K.; investigation, D.Z. and A.S.; resources, M.I. and A.A. ([PERSON]); data curation, G.K. and A.A. ([PERSON]); writing--original draft preparation, A.S.; writing--review and editing, A.A. ([PERSON]) and A.A. ([PERSON]); visualization, A.S.; supervision, M.I. and S.K.; project administration, S.K.; funding acquisition, M.I. and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant no. AP23488521).  
Not applicable.  
**Data Availability Statement**: All seismic data were downloaded through the EarthScope Consortium Web Services accessed on 15 January 2025 ([[https://www.iris.edu/app/station_monitor/](https://www.iris.edu/app/station_monitor/)]([https://www.iris.edu/app/station_monitor/](https://www.iris.edu/app/station_monitor/))). All data collected in this study can be provided directly by the corresponding author upon reasonable request.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **STA/LTA** & **PhaseNet** & **GPD** & **CNN** \\ \hline Accuracy & 0.75 & 0.939 & 0.8796 & 0.941 \\ Recall & 0.64 & 0.857 & 0.8038 & 0.891 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed model with traditional P-wave detection methods.  
**Acknowledgments:** I.M. thanks the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant no. AP23488521) for its support of that study. The facilities of EarthScope Consortium were used for access to waveforms, related metadata, and/or derived products used in this study. These services are funded through the National Science Foundation's Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Agreement EAR-1724509.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this article.