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The drawing used in the following exercise on rotating in 3D, ab24-j.dwg, is in the Drawings folder on the CD-ROM.

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Thus, the weight vector, W H can be found by the equation below. ,

which, in this numerical example, means 2 = 0 for all n. Here, F;' for the equidistant , design and for the optimal design are, respectively, equal to 1.37 x and 0.50 x which implies a reduction by a factor of 2.7. Figure 4.4. shows the contribution of each measurement point 2 on interval [-4,4] to Fa,F,, and F,, respectively. Note that the , origin contributes most to Fa, nothing at all to F, or F,. On the other hand, the points but that contribute substantially to F, or F do not do so to Fa. , In Example 4.20, the Cram&-Rao variance has been taken as optimality criterion for the design. If more than one parameter is unknown, the Cram&-Rao variance is replaced by the Cram&-Rao lower bound matrix. Then, the optimality criterion becomes a scalar function of the elements of the this matrix. Optimizing such criteria is the subject of the next subsection.

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Note that the plane wave migration method described above can be used to regularize the recorded data. As the data are read, the transform is computed in a distributed fashion by various compute nodes resulting in a regularly sampled plane wave data volume for imaging purposes. Depending on the data and the application ( nal imaging or velocity analysis), sparse subsets of the plane wave data may be

Tables 4.1.and 4.2.show the results of minimizing Q numerically for this choice. For comparison, the results for eight equidistant measurement points between -3 and 3 have been included. Table 4.1.shows that the reduction of the criterion is mainly caused by a substantial reduction of the Cramtr-Rao variance for a. Note that the optimal design, shown in Table 4.2., concentrated in four points: -1.71,-0.12,0.43, 1.72.At these is and points, two, three, one, and two observations are made, respectively.

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