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Computational Considerations for the Inversion of Gravity and Magnetic Potential Field Data

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RNTW01 - Rich and Nonlinear Tomography (RNT) in Radar, Astronomy and Geophysics

I will provide an overview of the challenges associated with solving the geophysics potential field data problem for inferring substructure from limited surface measurements. Considerations of computational efficiency and memory requirements are addressed. Iterative algorithms based on both randomized numerical linear algebra and Krylov methods will be discussed. Pros and cons of the approach(es) for obtaining reliable solutions for practical data sets are provided. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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