Deep Learning for Early Earthquake Detection: Application of Convolutional Neural Networks for P-Wave Detection
Zhexebay D. Skabylov A. Ibraimov M. Khokhlov S. Agishev A. Kudaibergenova G. Orazakova A. Agishev A.
April 2025Multidisciplinary Digital Publishing Institute (MDPI)
Applied Sciences (Switzerland)
2025#15Issue 7
Featured Application: Earthquake Detection, Earthquake Early Warning System (EWS), Processing of Seismic data. Early detection of earthquakes is essential for minimizing potential damage and ensuring public safety. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), provide a promising alternative for analyzing seismic waves. In contrast, traditional methods such as the short-term average/long-term average (STA/LTA) algorithm and the Akaike information criterion (AIC) have limitations in detecting primary (P) waves in high-noise conditions, caused by industrial and anthropogenic disturbances. This study presents a CNN-based automatic P-wave detection model tailored for the Almaty city region. The seismic dataset used in this research was obtained from the IRIS database and includes data collected from seven stations within a 333 km radius of Almaty, Kazakhstan. The proposed model achieves a recall rate of 89.1% and an accuracy of 94.1% compared to other deep learning-based models. Experimental results demonstrate that this method enhances the reliability of automatic early earthquake warning systems and improves the accuracy of P-wave detection. The research outputs presented for the local region are unique. Applying CNNs in seismic monitoring facilitates the development of efficient automated systems that minimize risks and improve response measures for natural disasters.
convolutional neural networks , deep learning , earthquake early warning , P-wave detection , seismic monitoring
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Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71/23 Al-Farabi Ave., Almaty, 050040, Kazakhstan
Data Science in Astrophysics Research Center, Almaty, 050040, Kazakhstan
Faculty of Physics and Technology
Data Science in Astrophysics Research Center
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