Dimensionality reduction in the Geostatistical approach for Hydraulic Tomography
نویسندگان
چکیده
The implementation of geostatistical approach to solve inverse problems [8], such as estimating hydraulic conductivity from measurements of head, is expensive for problems discretized on fine grids. Dimensionality reduction techniques such as representing the random field via Karhunen-Loève expansion, are frequently used in such a context. We show how to combine an efficient method to compute the expansion on an unstructured grid using Hierarchical matrices and a Gauss-Newton-Krylov approach for solving the inverse problem. We present preliminary results on a synthetic model problem arising from Hydraulic tomography.
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