Affiliation:
1. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
2. Dr. N.G.P. Institute of Technology, India
3. R.M.K. College of Engineering and Technology, India
Abstract
The chapter presents a comprehensive exploration of deep learning techniques applied to seismic pattern recognition and forecasting, aiming to advance our understanding of earthquake behavior. Beginning with a brief overview of the seismic cycle and its significance, the narrative delves into the challenges associated with accurately predicting the locations of seismic cycles. The role of deep learning in revolutionizing seismic forecasting is then elucidated, emphasizing its capacity to predict beyond single seismic cycles and under diverse conditions. The chapter further provides a mathematical algorithm outlining the step-by-step process, from data collection to model training, adaptation, and continuous monitoring.