A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information
Publication: Research - peer-review › Journal article – Annual report year: 2012
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A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information. / Lange, Katrine; Frydendall, Jan; Cordua, Knud Skou; Hansen, Thomas Mejer; Melnikova, Yulia; Mosegaard, Klaus.
In: Mathematical Geosciences, Vol. 44, No. 7, 2012, p. 783-803.Publication: Research - peer-review › Journal article – Annual report year: 2012
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TY - JOUR
T1 - A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information
A1 - Lange,Katrine
A1 - Frydendall,Jan
A1 - Cordua,Knud Skou
A1 - Hansen,Thomas Mejer
A1 - Melnikova,Yulia
A1 - Mosegaard,Klaus
AU - Lange,Katrine
AU - Frydendall,Jan
AU - Cordua,Knud Skou
AU - Hansen,Thomas Mejer
AU - Melnikova,Yulia
AU - Mosegaard,Klaus
PB - Springer Netherlands
PY - 2012
Y1 - 2012
N2 - The frequency matching method defines a closed form expression for a complex prior that quantifies the higher order statistics of a proposed solution model to an inverse problem. While existing solution methods to inverse problems are capable of sampling the solution space while taking into account arbitrarily complex a priori information defined by sample algorithms, it is not possible to directly compute the maximum a posteriori model, as the prior probability of a solution model cannot be expressed. We demonstrate how the frequency matching method enables us to compute the maximum a posteriori solution model to an inverse problem by using a priori information based on multiple point statistics learned from training images. We demonstrate the applicability of the suggested method on a synthetic tomographic crosshole inverse problem.
AB - The frequency matching method defines a closed form expression for a complex prior that quantifies the higher order statistics of a proposed solution model to an inverse problem. While existing solution methods to inverse problems are capable of sampling the solution space while taking into account arbitrarily complex a priori information defined by sample algorithms, it is not possible to directly compute the maximum a posteriori model, as the prior probability of a solution model cannot be expressed. We demonstrate how the frequency matching method enables us to compute the maximum a posteriori solution model to an inverse problem by using a priori information based on multiple point statistics learned from training images. We demonstrate the applicability of the suggested method on a synthetic tomographic crosshole inverse problem.
KW - Geostatistics
KW - Multiple point statistics
KW - Training image
KW - Maximum a posteriori solution
U2 - 10.1007/s11004-012-9417-2
DO - 10.1007/s11004-012-9417-2
JO - Mathematical Geosciences
JF - Mathematical Geosciences
SN - 1874-8961
IS - 7
VL - 44
SP - 783
EP - 803
ER -