Omar Alfarisi
Papers
4
Total Citations
48
H-Index
4
About
Omar Alfarisi is a pioneering researcher at the intersection of geophysics, machine learning, and planetary robotics. His work centers on decoding the hidden physics of porous media—specifically, how rock permeability and capillary pressure co-determine fluid flow in natural and extraterrestrial environments. Alfarisi’s major contribution is the development of novel 3D vision and machine learning frameworks that dramatically accelerate the analysis of heterogeneous rock fabrics. His “Morphology Decoder” (19 citations) replaces computationally expensive Lattice Boltzmann simulations with a rapid, AI-driven permeability quantifier, while his “3D Adapted Random Forest Vision” (3DARFV) outperforms deep learning semantic segmentation in both accuracy and energy efficiency for planetary exploration. By untangling the intertwined physics of capillary pressure and permeability (17 citations), Alfarisi has provided a foundational tool for geodynamics and reservoir engineering. His work not only reduces computing power consumption but also enables real-time robotic surveillance on other planets. With a growing citation footprint and a focus on high-performance computing, Alfarisi is shaping the future of autonomous planetary science and subsurface fluid dynamics.
Research Focus
Key Achievements
Top Papers
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