## 1 Introduction  
The prediction of earthquakes has long been a formidable challenge [1, 2, 3], owing to several factors. Firstly, seismic events are the result of intricate interactions between tectonic plates, faults, and other geological factors [4], rendering the accurate forecasting of their timing and magnitudes exceedingly challenging. Secondly, the paucity of long-term and extensive data poses a significant hurdle in earthquake prediction. Large earthquakes often recur at lengthy intervals (hundreds to thousands of years) [5], making it arduous to identify trends and patterns over a prolonged time frame [6]. Owing to the intricate geological interplay, the variability of seismic activity, the inadequacy of comprehensive data, and the current technological limitations, the prediction of earthquakes remains a complex and formidable field. Moreover, conventional prediction methods based on empirical (physical or statistical) models are often oversimplified and fallacious when applied to real-life scenarios [7]. With the rapid development of artificial intelligence (AI) in recent years, many research fields have been benefited, including earthquake prediction. At the core of AI lies machine learning, which plays an essential role in driving this transformation. Machine learning's ability to identify the corresponding functional relationships between vast amounts of data and corresponding labels is its primary advantage. These relationships can be high-dimensional and nonlinear, making it challenging for humans to comprehend, as is the case with earthquake prediction [8; 9]. Traditionally, experts' experiences formed the basis of earthquake prediction, which led to random and uncertain outcomes. However, the application of machine learning to earthquake prediction provides a promising approach to achieving more accurate and reliable results. As early as the 1990s, some scholars proposed the use of machine learning in the area of research in seismology [10]. Currently, various machine learning methods have been applied to seismic classification and location [11; 12; 13; 14], seismic event prediction [15; 16; 17], seismic early warning [18], seismic exploration [19], slow slip event detection [20], and tomography [21], achieving promising preliminary results in these research fields. Meanwhile, the rapid deployment of long-term seismic monitoring, providing a huge amount of seismic dataset information, accelerates the application of machine learning methods in seismology [8; 9; 22]. In the research of time series and magnitude prediction, neural networks, including LSTM and convolution neural networks, have been widely used in recent years [23; 24]. In [25], the authors proposed a novel approach to earthquake prediction using LSTM networks to capture spatiotemporal correlations among earthquakes in different locations. Their simulation results demonstrate that their method outperforms traditional approaches. The use of seismicity indicators as inputs in machine learning classifiers has been shown to improve accuracy in earthquake prediction [26]. Results from applying this methodology to four cities in Chile demonstrate that robust predictions can be made by exhaustively exploring how certain parameters should be set up. In [27], a proposed methodology based on the computation of seismic indicators and GP-AdaBoost classification has been trained and tested for three regions: the Hindu Kush, Chile, and Southern California. The obtained prediction results for these regions exhibit improvement when compared with already available studies. The application of artificial neural networks (ANNs) to earthquake prediction is explored in [28]. The results from the application of ANNs to Chile and the Iberian peninsula are presented, along with a comparative analysis with other well-known classifiers. The conclusion is that the use of a new set of inputs improved all classifiers, but the ANN obtained better results than any other classifier.