Earthquake and blast recognition based on CEEMDAN multiscale fuzzy entropy and NSGAIII optimized 1D-CNN neural networks

This study proposes an enhanced method for natural earthquake and artificial explosion recognition, which comprises two parts, namely the multiscale fuzzy entropy (MFE) feature extraction of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the non-dominated sorting genetic algorithm III (NSGAIII) optimization of the one-dimensional convolutional neural network (1D-CNN). CEEMDAN decomposes earthquake signals into initial functions (intrinsic mode functions) and extracts fuzzy entropy features to construct a discriminative time-frequency representation. The hyperparameters of 1D-CNN (minimum batch size, initial learning rate, and learning rate drop factor) were optimized by NSGAIII, using a dual objective function to minimize mean squared error and maximize R2. Tests on 1000 earthquake events (883 earthquakes and 117 explosions) showed that the model has an accuracy of 97.82%, which is better than traditional networks (1D-CNN, generalized regression neural network, probabilistic neural network, back propagation neural network, and radial basis function neural network) and has better regression indicators (mean absolute error = 0.0795, root mean squared error = 0.1302, R2 = 0.7361). The Adam optimization algorithm achieved peak performance (99.50%), significantly surpassing SGD-M and RMSprop. This framework effectively solves the small sample and high-dimensional classification problems in earthquake monitoring and improves the automatic event detection capability of the early warning system.