## 1 Introduction  
The earthquake catalog is a crucial foundation for seismic research. A high-resolution regional catalog includes a large number of highly precisely located earthquakes. The greater the number of precisely located earthquake events, the more it helps to clearly reveal the physical structure of the underground media. This is crucial for various seismological research fields, such as seismic tomography, fault zone structures, stress states of the Earth's interior, and seismic hazard assessments [1; 2; 3]. Generally, the process of constructing a seismic catalog involves multiple steps, including earthquake detection, seismic phase picking, phase association, and earthquake localization. Since the inception of seismology, seismologists have been dedicated to detecting more events from noise-rich continuous seismic waveforms, more precisely picking seismic phase arrivals, and more precisely locating earthquakes to construct high-resolution catalogs [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. However, manual phase-picking methods are not only inefficient but also susceptible to picking errors. Moreover, traditional rule-based automated algorithms face challenges in balancing efficiency, accuracy, and completeness. For instance, a class of automated algorithms based on energy characteristic functions struggles to correctly identify seismic signals characterized by impulsive noise [13]. On the other hand, algorithms based on waveform similarity principles heavily rely on the diversity of prior template events, with low computational efficiency, making them less suitable for scenarios involving large datasets [11; 12]. The key challenge encountered when using traditional automated algorithms to address seismic phase recognition issues lies in the inherent difficulty of mathematically describing these problems [14]. Deep learning technology allows computational models composed of multiple processing layers to learn mathematical representations with multiple abstraction levels. Its advantage lies in employing a data-driven learning approach to find solutions to problems [15]. This characteristic is particularly suitable for addressing phase-picking issues. In recent years, the technology used to construct seismic catalogs based on deep learning has experienced rapid development. This includes not only deep learning-based techniques for earthquake detection [16; 17; 18], seismic phase-picking [19; 18], phase association [20], and automatic earthquake location workflows [21; 22], but also the gradual improvement of AI earthquake benchmark datasets [23; 24]. [PERSON] et al. [16] first trained a deep convolutional neural network (CNN) with eight hidden layers and used it to detect roughly located earthquakes in the Oklahoma area. [PERSON] et al. [17] first designed a hybrid model integrating a CNN with a recurrent neural network (RNN). This model utilized a CNN to classify earthquake events and noise, while an RNN was employed for the phase-picking of P- and S-waves within the time windows of the seismic events. For regression-based solutions, [PERSON] and [PERSON] [19] designed a U-shaped deep neural network with four downsampling and four upsampling processes. This network (PhaseNet) treated the phase-picking of seismic waves as a probability distribution of P, S, and noise at each point within a time series window, achieving pixel-level identification in seismic waveform data for the first time. They trained their PhaseNet with over 700,000 earthquake events from California, enabling the high-precision picking of P and S wave arrivals across different instrument types. [PERSON] et al. [18] first introduced the attention mechanism into the problem of phase detection in seismology and presented a global deep learning model (EQtransformer, Stanford, CA, USA) for simultaneous earthquake detection and phase-picking. These four AI models represent the four types of neural network structures commonly used in seismology.