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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}](img13.gif)
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}](img14.gif)
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}](img15.gif)
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
Figure 8 Nearest neighbor in time, linear
interpolation in space. The function is approximated by linear interpolation
in space within each cell.
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
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.
Next: Bilinear interpolation in time
Up: INTERPOLATION STRATEGIES
Previous: Nearest neighbor in time
Stanford Exploration Project
11/17/1997