Ground Motion

Ground motion research focuses on accurately predicting and understanding the shaking of the ground during earthquakes, crucial for seismic hazard assessment and structural design. Current research heavily utilizes machine learning, employing diverse architectures like generative adversarial networks, convolutional long short-term memory networks, and deep neural networks to model complex wave propagation and synthesize realistic ground motion scenarios, often surpassing traditional physics-based or empirical models in accuracy and efficiency. These advancements improve earthquake early warning systems, enhance structural resilience through improved design codes, and enable more robust simulations for robotic locomotion in dynamic environments.