A Hybrid Deep Learning Approach with LSTM and CNN for Enhanced Earthquake Prediction

Abstract

Predicting earthquakes remains a challenging task due to the implicit and nonlinear nature of seismic activity. Traditional methods often struggle to capture the complex patterns underlying seismic events. This study investigates the application of deep learning techniques, specifically a hybrid model combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to enhance earthquake prediction accuracy.The proposed approach involves six key stages: (1) collection of historical seismic activity data, (2) comprehensive data preprocessing, (3) exploratory data analysis to identify underlying patterns, (4) characterization using seismicity indicators, (5) implementation of the hybrid neural network model, and (6) evaluation of the model's performance in recognizing significant seismic trends. The model leverages diverse datasets encompassing geological and seismic characteristics to enhance its robustness.Experimental results reveal that the hybrid LSTM-CNN model achieves superior predictive accuracy, evidenced by a Mean Squared Error (MSE) of 0.62 and a Coefficient of Determination (R²) of 0.91. The LSTM component effectively captures temporal dependencies in the time-series data, while the CNN component identifies spatial features and seismic patterns. This dual capability significantly outperforms conventional approaches and provides deeper insights into earthquake dynamics.The findings of this study not only contribute to advancing earthquake prediction methodologies but also highlight the potential for transitioning towards image-based seismic data analysis. Such advancements open new opportunities for integrating machine learning with geophysical sciences to improve disaster preparedness and mitigation efforts.

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