## 1 Introduction
[16] proposed a CNN-BiLSTM-AM model combined with ZOH technology to predict the maximum magnitude and the number of earthquakes for the next month. In a study by [PERSON] et al., they predicted the annual maximum earthquake magnitude in 10 different regions by considering the correlation between the number of earthquakes and the annual maximum magnitude. The Multi-objective Information Fuzzy Network (M-IFN) achieved the most accurate results (AUC = 0.698) [17]. [PERSON] et al. [18] utilized artificial neural networks (ANNs) to demonstrate good performance in predicting earthquake acceleration, depth, and velocity in the Terengganu region. [PERSON] et al. [19] proposed a purely data-driven deep learning model called EPT, which takes into account both local and overall seismic information. Experimental results on datasets from five provinces in China achieved an accuracy rate of 90%. In summary, it is feasible to use various machine learning and deep learning models for earthquake prediction. Many researchers have achieved certain results in earthquake prediction by employing different models and datasets [20; 21; 22; 23; 24]. They all directly utilize and predict seismic magnitude data. However, due to the poor temporal characteristics of earthquake data, they often require more data or more complex models to achieve good results in earthquake prediction. [PERSON] et al. [25] proposed a signal processing method in 2014 called VMD. This method can effectively handle nonlinear and non-stationary signals. [PERSON] et al. [26] proposed a seismic data denoising algorithm that combines VMD and Savitzky-Golay (SG) filtering. This method was applied to the 2015 Nepal earthquake and improved the signal-to-noise ratio while preserving significant features. [PERSON] et al. [27] used Empirical Mode Decomposition (EMD) for analyzing magnitude time series of global earthquakes and concluded that similar results to natural time analyses of global earthquakes can be obtained at mesoscale time series. [PERSON] et al. [28] demonstrated that the Intrinsic Mode Functions (IMFs) obtained through VMD provide a more organized way to understand the frequency peak amplitudes and energy of accelerations related to earthquakes, thereby offering a better explanation of seismic events. [PERSON] et al. proposed a VMD method combined with a GRU-LUBE deep learning model based on machine learning theory. This algorithm enhances data correlation and extracts more comprehensive pre-earthquake anomalies than previous methods from the borehole strain data of two major earthquakes in Sichuan's Wenchuan and Lushan regions. Furthermore, the anomalies observed in the pre-earthquake borehole strain data for both earthquakes were similar [29]. Based on the above research, this paper applies the VMD method to the monthly maximum magnitude data in the North-South Seismic Belt region. It then processes the multiple modes obtained from VMD using four deep learning models: LSTM, BiLSTM, ATT-LSTM, and Attention-based Bidirectional Long Short-Term Memory (ATT-BiLSTM). Hyperparameter analysis is conducted for each deep learning model, and the optimal parameters for each mode are selected by comparing the minimum MSE of each model. After obtaining the results for each mode, they are summed to form the final prediction result. The effectiveness of the models is evaluated by comparing the PA, FAR, and MR among the different models. The final conclusion is that decomposing magnitude data using the VMD method is feasible. The rest of the paper is organized as follows: Section 2 provides an overview of the study area, the data used, and the basic data processing. Section 3 describes the models and evaluation metrics used in this paper. Section 4 details the experimental process. Section 5 presents the experimental results. Section 6 discusses the results of the study. Section 7 summarizes the conclusions and outlines future work in this area.