Smoothing
(SM)
Averages the values in the grid to produce the desired result. |
Convolution
Mask:| 1.0 | 1.0 | 1.0 | | 1.0 | 1.0 | 1.0 | | 1.0 | 1.0 | 1.0 |
|
Weighted
Smoothing (WS)
Averages the values in the surface to produce the result, but biases
the
calculation so that closer bins have a greater contribution to the
final result. |
Convolution
Mask:| 0.25 | 0.5 | 0.25 | | 0.5 | 1.0 | 0.5 | | 0.25 | 0.5 | 0.25 |
|
Median
Smoothing (MS)
Computes the median value in the surface to produce
the desired result. |
Values within the sample window are sorted based on increasing value and then the median number is selected from the center of the sorted values. |
Dip
(DIP)
Computes the magnitude of the dip or slope of the surface over
the grid size at each bin. |
Convolution
Mask: X| 0.0 | 0.0 | 0.0 | | -1.0 | 0.0 | 1.0 | | 0.0 | 0.0 | 0.0 |
| Y| 0.0 | -1.0 | 0.0 | | 0.0 | 0.0 | 0.0 | | 0.0 | 1.0 | 0.0 |
|
|
Azimuth
(AZM)
Computes the azimuth, or dip/slope direction, at each bin referenced to
the inline direction. The
result is in degrees. |
Convolution
Mask: X| -1.0 | 0.0 | 1.0 | | -2.0 | 0.0 | 2.0 | | -1.0 | 0.0 | 1.0 |
| Y| -1.0 | -2.0 | -1.0 | | 0.0 | 0.0 | 0.0 | | 1.0 | 2.0 | 1.0 |
|
|
Azimuth
(True North) (ASM-TN)
Computes the azimuth, or dip direction, at each
bin. Apply a correction factor so the azimuth points north
rather than in the inline direction. The result is in
degrees. Use this if you are computing and mapping the azimuths of
several surfaces (over different data sets) at the same time. |
Convolution
Mask: X| -1.0 | 0.0 | 1.0 | | -2.0 | 0.0 | 2.0 | | -1.0 | 0.0 | 1.0 |
| Y| -1.0 | -2.0 | -1.0 | | 0.0 | 0.0 | 0.0 | | 1.0 | 2.0 | 1.0 |
|
|
Gradient
(GRD)
Calculate the magnitude of the dip. Useful for fault
detection. |
Convolution
Mask: X| -1.0 | 0.0 | 1.0 | | -2.0 | 0.0 | 2.0 | | -1.0 | 0.0 | 1.0 |
| Y| -1.0 | -2.0 | -1.0 | | 0.0 | 0.0 | 0.0 | | 1.0 | 2.0 | 1.0 |
|
|
Edge
Detection Inline (EDGE-IN)
Enhance the edges in the inline direction. |
Convolution
Mask:| -1.0 | -1.0 | -1.0 | | 2.0 | 2.0 | 2.0 | | -1.0 | -1.0 | -1.0 |
|
Edge
Detection Crossline (EDGE-CL)
Enhance the edges in the crossline
direction. |
Convolution
Mask:| -1.0 | 2.0 | -1.0 | | -1.0 | 2.0 | -1.0 | | -1.0 | 2.0 | -1.0 |
|
Edge
Detection Diagonal Down (EDGE-DD)
Enhance the edges from the upper
left to lower right direction. |
Convolution
Mask:| 2.0 | -1.0 | -1.0 | | -1.0 | 2.0 | -1.0 | | -1.0 | -1.0 | 2.0 |
|
Edge
Detection Diagonal Up (EDGE-DU)
Enhance the edges from the upper
right to lower left direction. |
Convolution
Mask:| -1.0 | -1.0 | 2.0 | | -1.0 | 2.0 | -1.0 | | 2.0 | -1.0 | -1.0 |
|
Difference
Inline (DIFF)
Calculate the difference between two bins in the
inline direction. Useful for edge detection. |
Convolution
Mask:| 0.0 | 0.0 | 0.0 | | -1.0 | 1.0 | 0.0 | | 0.0 | 0.0 | 0.0 |
|
Difference
Crossline (DIFFC))
Calculate the difference between two bins in the
crossline direction. Useful for edge detection. |
Convolution
Mask:| 0.0 | -1.0 | 0.0 | | 0.0 | 1.0 | 0.0 | | 0.0 | 0.0 | 0.0 |
|
Laplacian
(LAP)
Computes the difference between a bin and its neighbors.
This shows the changes in slope, but can be effected by
noise. Useful for detecting both sides of an edge. |
Convolution
Mask:| -1/8 | -1/8 | -1/8 | | -1/8 | 1.0 | -1/8 | | -1/8 | -1/8 | -1/8 |
|
Mean
Curvature (CRV-MEAN)
The average of two orthogonal curvatures at a bin location. Used
primarily to derive other curvature attributes but the
results are similar to Maximum Curvature.
|
Kmean
= a(1+e2
+ b(1 + d2):
cde
(1 + d2
+ e2)
¾
where the surface is
represented by:
z = ax2
+ by2
+ cxy + dx + ey + f |
Gaussian
Curvature (CRV-GAUSSE)
The product of the principal curvatures (minimum/maximum curvatures) at
a bin location. Typically this is used to derive other
curvature attributes. It gives the measure of the distortion of a
surface and can show fractures but not faulting. |
Kgaus
=
4ab: c2
(1 + d2
+ e2) 2
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Maximum
Curvature (CRV-MAX)
The largest absolute curvature at any point. Used to delineate faults
and their orientation. |
Kmax
= Kmean
+
sqrt( Kmean2:
Kgaus)
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Minimum
Curvature (CRV-MIN)
The curvature that is perpendicular to the large absolute curvature
(maximum curvature). Can be used to show faults and
fracturing. |
Kmin
= Kmean:
sqrt( Kmean2:
Kgaus)
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Most
Positive Curvature (CRV-POS)
The greatest positive curvature of a surface at a bin location. Used to
show anticlines and domal features. |
Kmp
= (a + b) + sqrt( (a: b)2
+ c2
)
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Most
Negative Curvature (CRV-NEG)
The greatest negative curvature of a surface at a bin location. Used to
show synclines and bowls. |
Kmn
= (a + b): sqrt( (a: b)2
+ c2
)
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Dip
Curvature (CRV-DIP)
The curvature in the direction of maximum dip. This is used to enhance
differentially compacted features, or the throw and direction of faults. |
Kdip
=
2(ad2
+ be2
+ cde)
(d2
+ e2)(1
+ d2
+ e2)3/2
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Strike
Curvature (CRV-STRK)
Curvature in the perpendicular direction of the dip curvature. Can be
used to show open fracturing. |
Kstrike
= 2(ae2
+ bd2
+ cde)
(d2
+ e2)(1
+ d2
+ e2)1/2
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
Shape
Index (CRV-SI)
This is the qualitative description of the shape, that is independent
of the scale. |
KshapeIndex=
2 × tan-1
[ Kmax
+ Kmin ]
pi
Kmax:
Kmin
where the surface is represented by:
z = ax2
+ by2
+ cxy + dx
+ ey + f
|
