## 1 Introduction
(2023) [10] evaluated existing P-wave detection methods and algorithms for estimating earthquake magnitude, identifying three main categories: source-based algorithms, wavefield-based algorithms, and local measurement-based algorithms. They also highlighted the increasing interest in recent years in using deep learning methods to enhance P-wave detection accuracy and minimize false alarm rates. With the advancement of machine learning, neural network models and classification algorithms have increasingly been applied in seismology. As a result, numerous studies have explored using artificial neural networks (ANNs) for seismic data analysis. For example, ref. [11] investigated the potential of ANNs for classifying dynamic features, demonstrating their applicability to seismic data processing. In [12], the application of convolutional neural networks (CNNs) to identify seismic waves and filter out noise was demonstrated. Ref. [8] highlighted the importance of integrating machine learning methods to enhance prediction accuracy and seismic risk assessment. It is essential to note that the successful application of ANNs in this field requires theoretical advancements and practical implementation. It includes the development of specialized software solutions and hardware systems for real-time seismic activity monitoring [12]. The expansion of machine learning techniques has led to the introduction of various approaches, such as the threshold processing algorithm [13], supervised learning based on classified signals from microseismic monitoring systems [14], the K-means clustering algorithm [15; 16], support vector machines (SVMs) [17], wavelet transformation [18], and template matching [19]. However, traditional machine learning methods often require manual feature selection, which can be labor-intensive and not always efficient [20]. These limitations highlight the need for automated feature extraction and deep learning-based approaches to improve seismic event detection and classification. Modern machine learning technologies and intense learning are opening new frontiers in P-wave detection and are developing effective early warning systems. Artificial neural networks can process large volumes of data and identify complex patterns that may not be apparent through traditional analytical methods. For example, recurrent neural networks (RNNs) enable the modeling of temporal dependencies within seismic data, which is particularly crucial given that the temporal characteristics of seismic signals play a key role in accurate detection [21]. ANN-based systems have the potential to significantly improve both the accuracy and speed of P-wave detection, thus improving the overall efficiency of early warning systems [3]. One of the modern methods for seismic wave detection is the capsule neural network, which demonstrates high accuracy and low false alarm rates. However, its primary drawbacks include relatively slow training and testing speeds, which limits its practical applicability to real-time seismic monitoring systems [22]. In 2019, the PhaseNet neural network model was introduced to detect P waves and S waves. PhaseNet utilizes three-component seismic waveforms as input data and generates probability distributions for P-wave arrivals, S-wave arrivals, and noise as output data [23]. Deep learning models, such as recurrent neural networks, utilize multiple hidden layers to automatically extract meaningful features from raw data, enabling them to process complex time series [24] effectively. This capability makes deep models particularly suitable for seismic data analysis, where it is essential to capture all relevant information contained in waveform patterns [25]. Furthermore, ref. [26] presented a machine learning model based on historical earthquake data for aftershock prediction, contributing to improved seismic risk assessment. Another CNN-based approach for real-time P-wave detection in earthquake early warning systems is Dpick.