Dimensionality-reduced estimation of primaries by sparse inversion
Data-driven methods—such as the estimation of primaries by sparse inversion suffer from the 'curse of dimensionality’ that leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensional...
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| Language: | English |
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University of British Columbia
2012
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| Online Access: | http://hdl.handle.net/2429/40723 |