** Next:** EXAMPLES
** Up:** METHODOLOGY
** Previous:** Details of the algorithm

On first consideration, the NW algorithm seems ill-suited to the seismic case, primarily because
seismic amplitude data is continuous, not discrete. To utilize the machinery of the NW
algorithm we consider the histogram of data amplitudes on the trace pair and form a set of
bins. That is to say, all of the floating point amplitudes are partitioned into a small number of
intervals (20 in the examples given below), and the similarity matrix is formed by the equation 10
operating on the binned amplitudes.
An important aspect of the algorithm is that a global optimum alignment function is found
independent of the similarity measure that is used. To test feasibility, we used a one point
similarity that captures amplitude differences. However we could easily have worked with a twopoint
measure to emphasize slope similarity, or three points to match curvature. Extending this
idea, one could work with short window cross correlations to fill the similarity
matrix similar Martinson and Hopper (1992). Clearly, any of these more ambitious similarity measures
would increase the cost of the algorithm. In any case, the NW algorithm guarantees a global
optimum alignment solution using any similarity matrix as input.
As a final comment, we note that any number of traces
that require alignment can be processed as a cascaded
series of pairwise problems.
Thus there is no loss of generality in discussing
just the pairwise problem.

** Next:** EXAMPLES
** Up:** METHODOLOGY
** Previous:** Details of the algorithm
Stanford Exploration Project

11/11/2002