A Pattern Recognition Approach for Automatic Horizon
Picking in 3-D
Seismic
Data![Next Hit](/images/arrow_right.gif)
Yu, Yingwei 1; Kelley, Cliff 1; Mardanova,
Irina 1
(1)Seismic
Micro-Technology, Inc., Houston, TX.
We present a new approach to auto pick horizons in 3D seismic
data
using a pattern recognition technique.
This method was used as the basis of an innovative autotracking
technology that has been utilized and proven by the interpretation
community
for almost two years.
In the practice of horizon picking, conventional methods usually
employ a window-based approach in searching extrema. This approach only scans
the adjacent trace vertically within a time window, while ignoring the lateral
continuity. The very limited window context often incurs the “off cycle”
effect, which means that the extrema points are incorrectly linked across
seismic
phase cycles. This effect can be more severe in
seismic
data
with
high-dip geologic structure. To preserve the lateral continuity of horizon
picking, one needs to examine the
seismic
data
pattern in a range of the
neighborhood. The proposed method utilizes the context information to predict
the horizon trend. We create a 3D
data
set constructed of directional pointers
defined at every
seismic
data
sample. It helps to determine the
data
pattern
and direct the trace selection algorithm through the
seismic
volume. Combining
the pointers set and a confidence-based trace selection mechanism helps to
optimize the 3D horizon autopicking and obtain more accurate results compared
to other conventional algorithms.
AAPG Search and Discovery Article #90135©2011 AAPG International Conference and Exhibition, Milan, Italy, 23-26 October 2011.