Unveiling Earth's Rhythms: Deep Learning Techniques for Forecasting Seismic Cycle Locations

Introduction

The seismic cycle is a pivotal concept in seismology, embodying the intricate dynamics of Earth's crustal movements that culminate in the occurrence of earthquakes. This cyclic process is inherently linked to the broader framework of plate tectonics, where Earth's lithospheric plates continuously interact along their boundaries. These interactions can manifest as convergent collisions, divergent separations, or lateral sliding, setting the stage for stress accumulation within the Earth's crust (Jena et al., 2020). Over time, this stress builds up along faults and plate boundaries, deforming rocks and storing potential energy. The seismic cycle reaches its climax when the accumulated stress exceeds the strength of the rocks, resulting in a sudden release of energy through elastic rebound (Asim et al., 2018). This release, manifested as seismic waves, is the essence of an earthquake. Understanding the seismic cycle is crucial for deciphering earthquake behavior and enhancing our ability to predict and mitigate seismic hazards. By comprehending the patterns of stress accumulation, deformation, and energy release, scientists gain insights into the mechanisms underlying seismic events (Ganguly et al., 2019). This knowledge not only contributes to the development of more accurate earthquake forecasts but also aids in assessing the potential risks associated with specific tectonic regions. As societies strive to minimize the impact of earthquakes on human lives and infrastructure, unraveling the complexities of the seismic cycle stands as a foundational step towards achieving a more resilient and prepared global community.