Machine Contouring
: Reading Between the Lines
Jeffrey M. Yarus, Aaron Brown
The wide availability of microcomputers and inexpensive software has
encouraged geologists to use contouring
programs. These programs are generally
based on interpolation techniques that include inverse distance, gradient
projection, polynomial fit, and triangulation. Most programs produce in contour
patterns that are highly at variance to the real
data
and can lead to very
serious and expensive mistakes. As an example, algorithms were tested on simple
known geometric surfaces: inclined, unimodal, bimodal, and faulted. Surfaces
were sampled using different sampling techniques: equally spaced, linear point,
random, and clustered.
In most cases, results showed false structural variations such as closure, nosing, and structure orientations which were erroneous. Better maps were produced with extensive manipulations of key parameters including grid size and weighting exponents.
When a priori knowledge of the surface is not known, as with real data
,
results indicate that in some cases satisfactory
contouring
can be generated
using the above-described surfaces as surrogate mapping horizons sampled by the
real
data
array. In this way, values for key parameters can be estimated prior
to mapping the real
data
. Although this method offers no guarantees, it does
tend to eliminate artifacts imposed on a surface by the algorithm.
Unless extreme caution is exercised when using machine contouring
algorithms
or better algorithms become available, one should rely on the hand
contouring
methods of an experienced geologist when important economic decisions are at
stake.
AAPG Search and Discovery Article #91038©1987 AAPG Annual Convention, Los Angeles, California, June 7-10, 1987.