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Nearest neighbor in time, linear interpolation in space

If the function is approximated by linear interpolation in space then the integral has a different form. A single sample will influence the interpolated value within two regions. Each region is bounded by a neighboring trace and half the distance to the nearest time samples.

Within each regions the approximate function is,

\begin{displaymath}
\hat{f}( x, t ) = 
 \tilde{f}( ix-1, it ) \left[ 1 - \frac{x...
 ...}( ix, it )\left[ \frac{ x - x_{ix-1} }{x_{ix}-x_{ix-1}}\right]\end{displaymath}

and the integral of the approximate function is given by,

\begin{displaymath}
\int_{\xi_0}^{\xi_1} 
 \tilde{f}( ix-1, it ) \left[ 1 - \fra...
 ... it ) \left[ \frac{ x - x_{ix-1} }{x_{ix}-x_{ix-1}} \right]\,dx\end{displaymath}

This has two terms, one involving each sample value.

\begin{displaymath}
\frac{(\xi_1 - \xi_0 )}{( x_{ix-1}-x_{ix} )}
 \left[ \tilde{...
 ...tilde{f}( ix, it ) ( x_{ix-1} - \frac{\xi_0+\xi_1}{2} ) \right]\end{displaymath}

The weight for one sample point is the sum of the weights from the cell to its left and right. When calculating the integral along a path the paths fall into the same three classes as in the previous method. The entry and exit points of the path within a cell must be calculated, and the weights for the two samples that define the cell can then be applied. Figure [*] illustrates the weights for this method. Although this method is close to the triangle weighting method proposed by Claerbout, the weights in a sampled space are not exactly triangles.

 
nnt-lins
nnt-lins
Figure 8
Nearest neighbor in time, linear interpolation in space. The function is approximated by linear interpolation in space within each cell.
view

Figure [*] shows the approximation to the original function surface that is implied by this method. If the time sampling is sufficiently fine this method will give a reasonable approximation of the true surface. When the time sampling is larger some interpolation in time must be considered.

 
linxnnt-func
linxnnt-func
Figure 9
Input data sampled every 4ms in time and 25m in space and then interpolated using nearest neighbor in time and linear interpolation in space.
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previous up next print clean
Next: Bilinear interpolation in time Up: INTERPOLATION STRATEGIES Previous: Nearest neighbor in time
Stanford Exploration Project
11/17/1997