Computer Aided Drafting, Design and Manufacturing
Volume 24, Number 2, June 2014, Page 10
CADDM
Project Item: Supported by NSFC (No.61170005).
Corresponding author: CHE Xiang-jiu, E-mail: chexj@jlu.edu.cn.
The Coherence Cube Computing Method with Self-adaptive Time Window
Based on Wavelet Transform
LI Ying-qi, CHE Xiang-jiu
College of Computer Science and Technology, Jilin University, Jilin 130012, China.
Abstract: The coherence cube technology has become an important technology for the seismic attribute interpretation, which
extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channels to identify the fault form.
The coherence cube technology which uses constant time window lengths can not balance the shallow layers and the deep layers,
because the frequency band of seismic data varies with time. When analyzing the shallow layers, the time window will crossover a
lot of events, which will lead to weak focusing ability and failure to delineate the details. While the time window will not be long
enough for analyzing deep layers, which will lead to low accuracy because the coherences near the zero points of the events are
heavily influenced by noise. For solving the problem, we should make a research on the coherence cube technology with
self-adaptive time window. This paper determines the sample points' time window lengths in real time by computing the
instantaneous frequency bands with Wavelet Transformation, which gives a coherence computing method with the self-adaptive time
window lengths. The result shows that the coherence cube technology with self-adaptive time window based on Wavelet
Transformation improves the accuracy of fault identification, and supresses the noise effectively. The method combines the
advantages of long time window method and short time window method.
Key words: coherence cube; time window length; Wavelet Transformation; seismic attribute
1 Introduction
In recent years, we have achieved great
development in the seismic technology. The seismic
interpretation technology has become an important
technology for petroleum exploration industry. The
coherence cube technology has become one of the
most important methods to recognize fault form
、
rock
distribution by analyzing the similarities of adjacent
3-D seismic channels to prominent layer and structural
discontinuities. Bahorich and Fame developed the first
generation of coherence technology in 1995
[1]
, and
they applied it into the petroleum exploration industry.
Marfurt developed the second generation of coherence
technology in 1998
[2]
. One year later, Marfurt
developed the third generation of coherence algorithm
based on characteristic structure
[3]
, which had high
horizontal resolution and great noise immunity. Then
Coifman and Cohen developed the coherence cube
technology based on local structural entropy in 2002
[4]
.
Three years later, Lu Wenkai developed the coherence
cube technology based on high-level statistical
magnitude
[5]
, and Li developed the coherence cube
technology based on super-channel set and
Eigen-value structure analysis
[6]
.
The scientists developed different methods which
could detect seismic discontinuities to promote the
coherence technology, but there was little research to
be made on how to choose the time window lengths.
The researchers usually choose the proper time
window length which is neither too long nor too short
to compute the coherence result, which can suppress
the low coherence strip of rock layer, and get the high
resolution. However, the frequency band of seismic
data varies with time, the method which chooses the
constant time window length can not balance the
shallow layers(wide frequency band) and the deep
layers(low frequency band).So when analyzing the
shallow layers, the time window will crossover a lot of
events, which will lead to weak focusing ability and
failure to delineate the details. While the time window
is not long enough for analyzing deep layers, which