image processing: program skeletonize3d generating...

Image Processing: Program skeletonize3d

Attribute-Assisted Seismic Processing and Interpretation 18 October 2019

GENERATING SKELETONIZED FAULT AND UNCONFORMITY IMAGES – PROGRAM skeletonize3d

Contents

Overview ......................................................................................................................................... 1

Computation Flow Chart ................................................................................................................. 1

Running program skeletonize3d ..................................................................................................... 2

Mathematical basis of the 3D skeletonization ........................................................................... 6

A Skeletonization Workflow ....................................................................................................... 7

Example: West Cameron, Gulf of Mexico ............................................................................... 8

References .................................................................................................................................... 16

Overview For large datasets, hand-picking faults can be very time consuming, such that any means to accelerate or facilitate the process can be quite attractive. “Automatic” fault extraction in most commercial software packages requires that the seismic attribute be first smoothed and then skeletonized. One of the more commonly used algorithms is the Artificial Ants algorithm described by Randen et al. (2001). More recent innovations include an edge-detection algorithm described by Zhang et al. (2014) that generates skeletonized fault “sticks” on time slices. Wu and Hale (2015) describe a method that map intersecting faults based on Hale (2013) fault construction technique. The program skeletonized3d is routinely used to skeletonize discontinuous structures, such faults and unconformities. Using the fault dip magnitude and fault dip azimuth, we can trace faults and unconformities along orientation of structures. Then, by rejecting the lower anomalous candidate point of one analysis window, it will result skeletonized faults and unconformities images.

Computation Flow Chart We build a workflow to segment fault structures from 3D seismic amplitude dataset. Generally, we will sharpen the edges using an edge preserving structure-oriented filtering algorithm like program sof3d. For fault segmentation, we next generate a similarity attribute using program similarity3d. We then make these faults smoother and more continuous using program fault_enhancement, the output of which are the input to program skeletonize3d, including fault



probability, fault dip magnitude, and fault dip azimuth volumes. The output of skeletonize3d is a skeletonized fault (or depending on the input data, axial plane or unconformity) image.

Running program skeletonize3d The skeletonization program is located under the Image Processing tab -> skeltonize3d of the main aaspi_util window:



The skeletonize3d GUI then appears:

The user needs to specify the input (1) fault probability, (2) fault dip magnitude, and (3) fault dip azimuth volumes previously computed by program fault_enhancement - (4) a dip threshold, (5) and an amplitude threshold. Dip and amplitude threshold are used to define filters to reject lower anomalies. All the output values should be smaller than 1. Therefore, amplitude threshold should not be over than 1. In general, we can just use default value, which is 0.03. Modify the parameters you wish to change and click Execute.



When it completes, it appears like the above figure. In this example, we use 24 processors. Then, we can use program AASPI QC Plotting to show the results.



Mathematical basis of the 3D skeletonization

Key factors of imaging faults include a good edge detecting attribute, a smoothing filter, an

effective scanning method and a reliable interpolating tool. In order to automatically

skeletonize faults, we should first suppress the incoherent noise and enhance fault trends.

Then by knowing fault dip magnitude and fault dip azimuth, fault image points can be detected

along fault planes. Figure 1a indicates a 3*3*3 cubic analysis window, and the center red point

is the fault candidate point. For a fault plane (indicated by green square), fault dip magnitude

θ and fault strike ψ can be calculated by eigenvector analysis (Barnes, 2006; Machado et al.,

2016). Fault dip magnitude θ and fault strike ψ are used to describe fault plane orientation

and dipping angle. Generally, the fault dip magnitude θ and strike ψ of each point on the same

fault plane should be similar. After computing θ and ψ, we apply bilinear interpolation to the

center fault candidate point and interpolate the fault point onto four different surfaces shown

in Figure 1b (Surf1 and Surf2). Interpolated points S1 and S2 are on the line that is

perpendicular to the fault plane. Compared with other 2D fault skeletonization method, one

can skeletonize a fault not only from a horizontal plane (time slice), but also from a vertical

plane (vertical section). Faults can be thinned and smoothed. Other incoherent anomalies,

such as channels, and mass transport complexes, that have disorderly dip magnitude and dip

azimuth anomalies, will be rejected by this process.

Figure 1 (a) One fault point in a 3*3*3 window associated with fault dip magnitude and fault

dip azimuth; (b) bilinear interpolated four points. Line S1-S2 is perpendicular to the fault plane.



A Skeletonization Workflow

We built a generalized workflow for fault skeletonization, for which the workflow requires the application of programs dip3d, similarity3d, sof3d, spec_cwt, fault_enhancement, skeletonize3d. Before skeletonizing fault images, it is necessary to generate high resolution edge detecting attributes. We apply structure-oriented filter to smooth seismic amplitude, and reject coherent noise that result in higher signal-to-noise ratio discontinuous structures and steep dipping reflectors. The program sof3d would be applied twice to increase seismic resolution.



Example: West Cameron, Gulf of Mexico

The original input attribute of this workflow is a post stack seismic amplitude volume, which looks like the following image below:



The input attributes for program skeletonize3d are fault probability, fault dip magnitude, and fault dip azimuth. The energy ratio similarity and fault probability look like the following images:

Energy ratio similarity can highlight faults, however other discontinuous anomalies, such as incoherent noise, interfaces of two layers, and migrated artifacts, are also highlighted. Fault anomalies in energy ratio similarity, are discontinuous in vertical section, which is caused by



rectangular moving analysis windows. By applying program fault_enhancement, we can obtain the fault probability:

The above figure is the result of program fault enhancement.



The above figure shows fault probability with input energy ratio similarity after 2 iteration runs of program fault_enhancement. Fault anomalies in the fault probability are more continuous and much sharper than in the energy ratio similarity.



The above output skeletonized image can preserve fault anomalies, and reject other discontinuous anomalies.



Co-rendering the seismic amplitude with skeletonized image through vertical section. After fault skeletonized, faults are much thinner and sharper. What’s more, other structural discontinuities were also smoothed. Low dipping angle and low amplitude anomalies are smeared during skeletonization process.



Comparing seismic amplitude energy ratio similarity through time slices.



Comparing fault probability and fault skeletonized image through time slices:



Co-rendering the seismic amplitude with skeletonized image through time slice.

References

Barnes, A. E., 2006, A filter to improve seismic discontinuity data for fault interpretation: Geophysics, 71, 1-4.

Hale, 2013, Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images, Geophysics, 78, 33-43.

Machado, G., A. Alali, B. Hutchinson, O. Olorunsola, and K. J. Marfurt, 2016, Display and enhancement of volumetric fault images, Interpretation, 4, 51-61.

Randen, T., S.I. Pedersen, and L. Sønneland, 2001, Automatic extraction of fault surfaces from three-dimensional seismic data: Annual International Meeting, SEG, Expanded Abstract, 551–554.

Wu, X., and D. Hale, 2015, 3D seismic image processing for faults: Geophysics, 81, 1-11. Zhang, B., Y. liu, M. Pelissier, and N. Hemstra, 2014, Semiautomated fault interpretation based

on seismic attributes: Interpretation, 2, SA11-SA19.

image processing: program skeletonize3d generating...

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