Weekly Earthquake Prediction Algorithm Based on Dense Net

Earthquakes pose significant risks to life and property, making their prediction a crucial challenge. Traditional approaches to earthquake prediction suffer from limited generalization capabilities due to the influence of specific seismic zone characteristics and struggle with imbalanced seismic data management. Previous research has primarily relied on explicit seismic indicators designed by geologists or implicitly extracted feature vectors using deep learning techniques. However, effective fusion methods for utilizing the abundant information within earthquake data are lacking. To address these limitations, this study proposes a model decoupling methodology that separately trains a backbone network and a classification network. This approach specifically tackles the challenges posed by class imbalance in seismic data. Our research utilizes explicit seismic features derived from precursor signals and employs DenseNet as the backbone network to extract implicit features from seismic data. The fusion of explicit and implicit features enables accurate representation and comprehensive utilization of earthquake data. Evaluation metrics such as accuracy, recall rate, and F1-Score are employed to assess the performance of the proposed method. Experimental results validate its efficacy in earthquake prediction, with high accuracy rate (95.8%), recall rate (95.8%), and F1-Score (0.95). This research substantiates the effectiveness of our approach and its potential impact in the field of earthquake prediction.