Zhiang Chen, California Institute of Technology The intricate and dynamic nature of fault zones and fragile geological features has long fascinated geoscientists and researchers. Understanding these geological phenomena is crucial not only for scientific exploration but also for hazard assessment and resource management. Recently, the convergence of robotics and machine learning has given rise to a transformative practice called automated geoscience. This practice utilizes robotics to automate data collection and machine learning to automate data processing, liberating geoscientists from labor-intensive activities. Focusing on rock detection, mapping, and dynamics analysis, I present the applications of automated geoscience in fault zone mapping and fragile geological feature analysis. To explore the influence of rocky fault scarp development on rock trait distributions, I have developed a data-processing pipeline using UAVs and deep learning to segment dense rock distributions. This application provides a statistical approach for geomorphology studies. Precariously balanced rocks (PBRs) offer insights into ground motion constraints for hazard analysis. I have designed offboard and onboard methods for autonomous PBR detection and mapping. After mapping, I delve into PBR dynamics with a virtual shake robot simulating the dynamics of PBRs in overturning and large displacement processes with respect to various ground motions. The overturning and large displacement processes provide upper-bound and lower-bound ground motion constraints, respectively. Moving forward, I am integrating automated geoscience into broader studies on fault zone mapping and fragile geological feature analysis. My aim is to push this interdisciplinary research direction, offering potential advancements in hazard monitoring and prospecting.