Efficient geostatistical inversion of transient groundwater flow using preconditioned nonlinear conjugate gradients
We present a preconditioned conjugate gradient method for geostatistical inversion. The prior covariance matrix is used as preconditioner at negative computational cost. The approach incorporates linearized uncertainty quantification. The method is particularly efficient for the inversion of transie...
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| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
April 2017
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| In: |
Advances in water resources
Year: 2017, Volume: 102, Pages: 161-177 |
| ISSN: | 1872-9657 |
| DOI: | 10.1016/j.advwatres.2016.12.006 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1016/j.advwatres.2016.12.006
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| Author Notes: | Ole Klein, Olaf A. Cirpka, Peter Bastian, Olaf Ippisch |
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| 520 | |a We present a preconditioned conjugate gradient method for geostatistical inversion. The prior covariance matrix is used as preconditioner at negative computational cost. The approach incorporates linearized uncertainty quantification. The method is particularly efficient for the inversion of transient data. Transient inversion may speed up experiments and improve quality of inversion results. | ||
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