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OwnerSouvik Mukherjeeinventor
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A method and system for three-dimensional reconstruction of material properties of a target using remotely located physical sensors is disclosed. The special technique disclosed here, enables an order of magnitude improvement in computational speed and memory requirements over current state-of-the-art artificial intelligence-based systems. When compared against state-of-the-art methods that do not use artificial intelligence, the improvement in accuracy and resolution enables deployment of order of magnitude cheaper data acquisition systems and/or provide the practical capability to image targets previously considered out-of-bounds. The use cases include but are not limited to oil field application systems such as the monitoring of pipeline health and integrity, leak, and spill extent delineation, seismic imaging systems, and for applications in agriculture, medical imaging, unexploded ordnance detection, mining, wind energy foundation studies, geotechnical work, groundwater systems, environmental science and engineering, and other problems where remote sensing-based image reconstruction is utilized/needed.
1) A novel physics-based formulation of the input data from remote sensing imaging sensors that enable the deployment of one-dimensional vector based deep machine learning architectures for multidimensional image reconstruction tasks and solving of inverse problems.
04) Enable the solution of larger (by an order of magnitude) problems of the kind discussed in 01), 02) and 03) for a given computer system than what can be done using current state-of-art machine learning architectures.
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