Deep Learning Approaches for Earthquake Forecasting and Early Warning Systems in Seismically Active Regions

Abstract

Earthquakes pose a significant threat to human life and infrastructure, especially in seismically active regions. Over the past few decades, numerous attempts have been made to enhance the accuracy and timeliness of earthquake forecasting and early warning systems (EEWS). Traditional approaches rely heavily on geological, seismological, and statistical models that often fail to deliver reliable predictions. The rise of deep learning has introduced a paradigm shift by enabling models to extract complex spatiotemporal features from seismic signals, satellite data, and geophysical indicators. This paper reviews state-of-the-art deep learning approaches for earthquake forecasting and EEWS, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, generative adversarial networks (GANs), and A IoT integrations. We analyze twenty recent studies, highlighting their methodologies, advantages, and limitations. Furthermore, comparative study, recent technical challenges, and future research directions are presented. The findings emphasize that deep learning models significantly improve real-time detection, blind zone reduction, and public safety response mechanisms. However, challenges like data quality, model generalization, and real-time deployment persist. This review aims to provide researchers, seismologists, and policymakers with a comprehensive understanding of the current state and future scope of AI-powered earthquake prediction systems to mitigate natural disaster impacts effectively.