# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 1 Introduction
This method calculates seismic activity indicators for each time interval and their relationship with the latitude and longitude of the epicenter, using a recurrent neural network to study the timing of the next major earthquake. The model provides relatively accurate results in areas with abundant earthquake data. [PERSON] and [PERSON] (2019) used a BP network for earthquake prediction in the Himalayan belt. They collected various seismic features, including latitude, longitude, magnitude, b-value, and energy, with earthquake magnitude as the final output. The model demonstrated good predictive performance for earthquakes with magnitudes between 4 and 6. [PERSON] et al. (2020) used deep learning methods to predict aftershock locations by training a neural network on a dataset of mainshock-aftershock pairs. The study found that the learned aftershock patterns were physically interpretable, with factors such as the maximum change in shear stress, von [PERSON] yield criterion, and the sum of the absolute values of stress change tensors explaining most of the predictive variance. These insights offer a new approach to improving aftershock location predictions and identifying physical quantities that may control earthquake triggering during active seismic phases. [PERSON] et al. (2021) adopted an innovative approach by constructing a two-dimensional long short-term memory (LSTM) network model to capture and learn the complex spatiotemporal relationships between earthquakes in different locations. Simulation results showed that their two-dimensional input LSTM network could discover and utilize spatiotemporal correlations between earthquakes, leading to better predictions. [PERSON] et al. (2020) proposed an LSTM network model based on attention mechanisms to predict large earthquakes' time, magnitude, and location. Using an earthquake dataset from Japan, a region with high seismic activity, they found that the model significantly outperformed other methods, such as ANN, in terms of mean absolute error (MSE), root mean square error (RMSE), mean absolute error (MAE),R-squared, and accuracy, with MSE reduced by about 60%. The results demonstrate the model's potential and effectiveness in earthquake prediction. It is worth mentioning that, in addition to earthquake prediction, machine learning methods have also shown broad application prospects in other geophysical fields. In tsunami monitoring, by using a denoising autoencoder model, it is possible to predict the height of a tsunami triggered by an earthquake when it reaches the shore, thus providing more accurate forecasts for tsunami early warning and disaster assessment [23]. In volcanic activity monitoring, [PERSON]'s team applied transfer learning methods to test pre-trained networks, selecting the AlexNet network model that was most suitable for classification tasks. They used positive results as a new dataset to repeatedly train the neural network, with the final outcome enabling the possibility of automatic alarms for volcanic unrest [24]. In fault slip monitoring, in 2021, [PERSON] et al. [25] designed a deep autoencoder structure consisting of \(11\) pure convolutional layers. This model learns appropriate filters by examining the relationship between the time characteristics of deformation signals and atmospheric delay, eliminating atmospheric disturbances and extracting ground deformation from InSAR time series. The method was applied to InSAR data from the North Anatolian Fault, where a segment of 2 mm fault slip was re-identified near the previously identified 1 cm slow slip. The deep learning model determined the slip length to be 8.5 km, revealing a slow earthquake with an activity range twice as large as initially recognized. This study used a sliding window approach to calculate multiple seismic activity indicators as feature parameters based on earthquake catalog data.