Application of BP Neural Networks in Earthquake Prediction

In the field of earthquake prediction, BP neural networks are widely employed. These networks utilize the backpropagation algorithm for training and can forecast critical seismic parameters such as occurrence time, location, and magnitude. To address diverse prediction requirements, different training methods can be implemented—including Gradient Descent (GD), Adaptive Gradient Descent with Momentum (GDX), and the Levenberg-Marquardt (LM) algorithm. The GD method iteratively updates weights using the gradient of the loss function, while GDX enhances convergence by incorporating momentum to avoid local minima. In contrast, LM combines gradient descent and Gauss-Newton approximation, making it highly efficient for medium-sized datasets. Selection among these methods should consider factors like data volume, computational resources, and prediction accuracy targets. Implementation in frameworks like MATLAB or Python often involves key functions such as `trainlm` (for LM) or customizable backward pass loops for GD-based approaches. This overview provides technical and implementation perspectives to support earthquake prediction modeling efforts.