Marmousi model estimation using 2-D nonstationary PEFs: first test

F = Born Modeling Operator
A = 2-D PEF (Prediction error filter)

Three images:
  1. Marmousi true model
  2. Marmousi estimated without PEF:   L2 norm minimization of r(m) =   Fm-d.
  3. Marmousi estimated with 2-D PEF:  L2 norm minimization of r(m)=A(Fm-d).

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While iteration brings the residual downward, it brings the estimated model upward.   Without the PEF only 18% of the model is recovered.   The PEF brings it up to 31% of the true model.   Why not 100%?   In principle, the number of iterations required is 121x369=44,649.

See the book (chapter 3).

Antoine Guitton did all the computing from August 14 to September 14, 2018
Jon Claerbout struggled over the theory and presentation since finishing his 2014 textbook (GIEE).

See all Claerbout's books.