Inverse B-spline interpolation |

Now that we have all the problem pieces together, we can test the performance gain in the inverse interpolation problem (19)-(25) from the application of B-splines.

For a simple 1-D test, I chose the function shown in Figure 4, but sampled at irregular locations. To create two different regimes for the inverse interpolation problem, I chose 50 and 500 random locations. The two sets of points were interpolated to 500 and 50 regular samples respectively. The first test corresponds to an under-determined situation, while the second test is clearly over-determined. Figures 29 and 30 show the input data for the two test after normalized binning to the selected regular bins.

bin500
50 random points binned to 500 regular
grid points. The random data are used for testing inverse
interpolation in an under-determined situation.
Figure 29. | |
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bin50
500 random points binned to 50 regular
grid points. The random data are used for testing inverse
interpolation in an over-determined situation.
Figure 30. | |
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I solved system (19)-(25) by the iterative conjugate-gradient method, utilizing a recursive filter preconditioning (Fomel, 1997a) for faster convergence. The regularization operator was constructed by using the method of the previous subsection with the tension-spline differential equation (Fomel, 2000b; Smith and Wessel, 1990) and the tension parameter of .

The least-squares differences between the true and the estimated model are plotted in Figures 31 and 32. Observing the behavior of the model misfit versus the number of iterations and comparing simple linear interpolation with the third-order B-spline interpolation, we discover that

- In the under-determined case, both methods converge to the same final estimate, but B-spline inverse interpolation does it faster at earlier iterations. The total computational gain is not significant, because each B-spline iteration is more expensive than the corresponding linear interpolation iteration.
- In the over-determined case, both methods converge similarly at early iterations, but B-spline inverse interpolation results in a more accurate final estimate.

norm500
Model convergence in the
under-determined case. Dashed line: using linear
interpolation. Solid line: using third-order B-spline.
Figure 31. | |
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norm50
Model convergence in the
over-determined case. Dashed line: using linear
interpolation. Solid line: using third-order B-spline.
Figure 32. | |
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Inverse B-spline interpolation |

2014-02-15