Dr. Oommen’s research efforts focus on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types. To achieve his research interests, he has adopted an inter-disciplinary research approach from two main areas, specifically: aerial/satellite based remote sensing for obtaining data, and artificial intelligence/machine learning based methods for data processing and modeling.
Dr. Oommen is expanding his research to investigate future applications of satellite remote sensing and machine learning for geological engineering in the fields of geohazards and georesource characterization. His immediate goal is to verify the applicability of remote sensing techniques such as Differential Interferometric Synthetic Aperture Radar (DinSAR) and Light Detection and Ranging (LiDAR) as sustainable operational strategies for monitoring land subsidence. Land subsidence is often the surface expression of a variety of subsurface mechanisms such as lowering of water table, drainage, lateral flow, loading, vibration, and tectonic activity. Quantifying subsidence is critical for land use and infrastructure planning, health monitoring of engineered structures as well as for understanding the subsurface conditions.