Key research themes
1. How can joint inversion methods integrate multiple geophysical datasets to improve subsurface model resolution?
This research area focuses on developing inversion algorithms that jointly interpret different types of geophysical data (e.g., seismic, gravity, electromagnetic) under the assumption of shared subsurface structural features. The goal is to reduce non-uniqueness inherent in individual inversions by exploiting common model boundaries or structural information that links distinct datasets. Advancements here impact fields such as mineral exploration, subsurface characterization, and medical imaging.
2. What are novel algorithmic advances and applications in inverse kinematics for articulated systems?
This theme explores the development of new computational algorithms to solve inverse kinematics (IK) problems efficiently and realistically for articulated joint chains, with applications in robotics, computer graphics, and biomechanics. It includes analysis and improvements over classical methods like Cyclic Coordinate Descent (CCD), focusing on convergence speed, joint motion naturalness, and avoidance of undesirable rotations, which are critical for animation accuracy and robotic control.
3. How can onset time analysis and advanced seismic inversion improve fluid flow characterization in reservoirs?
This research focuses on innovative inversion methods leveraging onset times of seismic attribute changes—rather than magnitudes—to reduce sensitivity to rock physics uncertainties for monitoring fluid flow in subsurface reservoirs. Such approaches provide more robust imaging of saturation and pressure changes in enhanced oil recovery and other fluid injection contexts, enabling improved estimation of flow properties and dynamic characterization from seismic monitoring data.