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Interpretation of seismic subsurface images
can and ought to be aided by computer processing.
The delineation of faults
is a step towards general automatic information extraction
from seismic images.
Automatic seismic image analysis
would ease the tedious and time-consuming
extraction of geological information
from seismic image volumes by human experts.
Additionally, automatic analysis
could objectively and reproducibly compare
the effect of competing seismic image processes
on the interpretation of seismic data.
However, artificial intelligence experiences great difficulties
in the development of
visual scene recognition and interpretation ().
For continued research into automatic seismic image analysis,
I advocate
(1) a classification framework for seismic features and
(2) a sophisticated segmentation scheme.
I wonder if
basic statistical measures
- mean, mode, variance, autocorrelation, and grey level histograms -
can classify
seismic image features and their boundaries
- layered sediments, salt, base rock, off-lap surfaces, on-lap surfaces.
If such a classification is feasible, the subsurface could
be separated into amorphous
segments of similar, stationary statistics and, consequently,
regions of identical geology.
Interpreters of remote sensing data successfully use similar
classification and segmentation algorithms ().
Next: My investigations
Up: Detection of seismic discontinuities
Previous: Discontinuity, picking, interpolation, and
Stanford Exploration Project
3/8/1999