## 1 Introduction  
As a major natural disaster, earthquakes have the potential to inflict substantial damage on humans and infrastructure in a very short time. According to research, earthquakes not only cause direct damage to buildings and infrastructure but also trigger secondary disasters such as fires, tsunamis, and landslides, further exacerbating the losses [1]. Moreover, the impact of earthquakes is not limited to physical destruction but also has profound negative effects on socio-economic aspects, posing significant challenges to the recovery and reconstruction efforts in the affected areas and creating substantial safety risks for people's lives. Therefore, in regions where strong earthquakes frequently occur, earthquake prediction and research are crucial in reducing the damage caused by earthquakes. In recent years, machine learning and deep learning have achieved promising results in the field of prediction. Against this backdrop, many researchers are dedicated to developing and improving earthquake prediction models [2; 3; 4; 5]. In 2007, [PERSON] et al. [6] developed a scientific method for short-term earthquake prediction by converting earthquake catalogs into eight seismic activity parameters based on seismic statistical parameters and fundamental statistical characteristics in seismology. They used prior statistical models, including the Gutenberg-Richter relationship (G-R) and characteristic earthquakes, and combined these parameters with neural network models to predict the magnitude of the largest seismic event expected in the coming month for a specific region. Since the emergence of these seismic activity parameters, the rapid development in fields such as machine learning has led to the derivation of multiple new parameters, which have been applied using various models for earthquake prediction research. [PERSON] et al. [7; 8] summarized the parameters and models used, along with the results, and highlighted the general applicability and importance of these parameters in earthquake prediction. [PERSON] et al. [9] investigated the relationship between eight seismic activity parameters and earthquakes using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer models, achieving significant positive results for earthquakes with magnitudes ranging from _M_s 3.5 to 6.0 across multiple regions. [PERSON] et al. [10] used the eight indicators as explicit features, applied convolutional neural networks to extract implicit features, and effectively fused the explicit and implicit features of the earthquake data. [PERSON] et al. [11] used machine learning techniques to study eight seismic indicators calculated for the Hindu Kush region, achieving significant results in predicting earthquakes with magnitudes greater than or equal to _M_s 5.5. As research progresses, models, methods, and prediction indicators for earthquake forecasting are becoming increasingly diverse. [PERSON] et al. [12] utilized the spatial relationships between earthquakes to predict seismic events and achieved good results. [PERSON] et al. [13] utilized a combination of classification algorithms (CAs) and mathematical optimization algorithms (OAs) to predict earthquake magnitudes and locations, demonstrating that with sufficient data, deep learning models can forecast severe catastrophic events. [PERSON] et al. [14] used earthquake indicators from the Bangladesh earthquake catalog to predict the magnitude and location of earthquakes for the next month using the Attention-based Long Short-Term Memory (ATT-LSTM) model. Their results showed that the accuracy of magnitude prediction reached 74.67%, and the root mean square error (RMSE) for location prediction was 1.25. [PERSON] et al. [15] employed the CovLSTM network, fully considering the spatial correlation of global earthquake data for high-resolution earthquake prediction, achieving results with a higher resolution and accuracy compared to previous methods. [PERSON] et al.