Advances in Deep Learning for Earthquake Monitoring and Forecasting: Techniques, Applications, and Future Directions

Accurate anticipation and surveillance of seismic activities are crucial for safeguarding human life and infrastructure. The integration of advanced deep learning algorithms has emerged as a transformative approach, offering unprecedented precision in interpreting intricate seismic data patterns. This review delves into the spectrum of recent advancements in deep learning methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), among others. We scrutinize their implementation in seismic phase identification, event detection, and predictive modelling. Furthermore, we underscore the existing challenges and chart a course for future research, aiming to further integrate these algorithms into seismological practices.