2.4. Benchmark steering cube creation
In this appendix, the quality and speed of the available steering algorithms is compared in order to guide the user in his choice for the right steering cube algorithm. First, the results of a test on a standard dataset are presented to give an indication of the speed of steering cube calculation versus the chosen algorithm and calculation cube size parameter (where applicable). Then, a visual quality check on one of the inlines is presented. This is done by checking the crossline dip, which is directly extracted from the steering cube and by reviewing the steered similarity, which is using informations derived from the steering cube.
The final output quality was considered not only dependent on the steering algorithm. The size of the calculation cube plays an important role. Next, the application of an additional median dip filtering influences the output quality, and, finally, the use of dip limit.
2.4.1. Speed vs. algorithm and calculation cube size
In Figure 2-1 the relative calculation speed is displayed for the different algorithms. The test was done on a cluster of 6 computers using distributed computing. The input cube has 817811 traces. Each trace has 1551 samples with step of 4 ms, making a total of 6200 ms. Calculation speed is measured in traces per second.
2.4.2. Visual quality check
In the following sections, the crossline dip component of a steering cube created with the different algorithms is presented. The figures contain the results from the Precise FFT algorithm, the Combined FFT algorithm, the Standard FFT algorithm and the BG Fast Steering algorithm (for technical details see Section 2.2.2) for a cube size of 3. Notice that the nomenclature convention used is the following:
The FFT precise is called FFT+.
The FFT combined is called FFT++.
The standard steering algorithm is called FFT+++.
The calculation cube size is specified after the algorithm (e.g. FFT7, BG5, etc.).
The limit of dip is called maxdipXXX, where XXX is the limit in µs/m.
Additional filtering are called medXYZ, where X is the inline stepout, Y the crossline stepout and Z the sample stepout.
The inline itself is displayed in Figure 2-2 for reference. The inline was selected because many geological and seismic features are visible, which enable the user to evaluate the performance of the algorithm in different environments.
2.4.3. Crossline dip attribute
The crossline dip is one of the two steering cube component, together with inline dip, and is related to the dips projected in the crossline direction. In Figure 2-3 to Figure 2-5 the crossline dip is displayed for the algorithms mentioned in Figure 2-1 with cube sizes of 3, 5, and 7.
Figure 2-3. Crossline dip with (calculation cube size=3): precise steering (A), combined steering (B), standard steering (C) and BG steering (D).
Figure 2-4. Crossline dip with (calculation cube size=5): precise steering (A), combined steering (B), standard steering (C) and BG steering (D).
Figure 2-5. Crossline dip with (calculation cube size=7): precise steering (A), combined steering (B), standard steering (C) and BG steering (D).
From Figure 2-3 to Figure 2-5 you will notice that there is no significant difference between the combined steering algorithm (FFT++) and the precise steering algorithm (FFT+), both in speed and in accuracy. The standard steering algorithm (FFT+++) is fast but apparently often produces erroneous results in high dip areas. In order to avoid getting a noisy steering cube, the calculation cube size of the FFT algorithms has to be at least 5 or 7. The latter is our default setting. The BG algorithm has a different behaviour: a cube size of 3 seems to be sufficient, but the raw steering cube is useless and a median filtering appears to be mandatory.
2.4.4. Filtering of the steering cubes
Figure 2-6 and Figure 2-7 show the results after applying median filter with different stepouts to the steering cubes. As you can see, no lateral filtering and only a small vertical filtering gives the best result with the FFT algorithm.
The BG steering needs to be filtered in the lateral and vertical direction. The best results were obtained with median filter with stepouts 1 1 3. After filtering, the outputs of the precise steering with cube size 7 and median filtered with stepout of 2 only in the vertical direction (FFT7+ med002) and Fast BG steering median filtered with the step outs 1 1 and 3 (BG3 med113) are very similar in accuracy, but this latter is produced 10 times faster.
Figure 2-6. Filtering of FFT+ using calculation cube size = 7: raw (A), median filter with step outs 0 0 2 (B), median filter with step outs 0 0 4 (C), median filter with step outs 1 1 2 (D).
Figure 2-7. Filtering of BG steering using calculation cube size=3: raw (A), median filter with step outs 0 0 2 (B), median filter with step outs 1 1 1 (C), median filter with step outs 1 1 3 (D).
Figure 2-8 shows that adding a dip limit during the processing does not affect the speed of the algorithms. The final result is strictly identical when the dip is lower than the limit L, and the extreme values are rounded toward L. Using a limit requires a priori knowledge and at the end remains the choice of the interpreter.
Figure 2-8. Filtering of FFT+ using calculation cube size=7: median filter with step outs 0 0 2 (A) median filter with step outs 0 0 2 and maximum dip of 300 (B), and filtering of BG steering using calculation cube size=3: median filter with step outs 1 1 3 (C), median filter with step outs 1 1 3 and maximum dip of 300 (D).
2.4.5. Steered similarity attribute
In Figure 2-9 is displayed the similarity attribute for the algorithms FFT+ and BG. As an extra reference the non-steered similarity is added. All figures were calculated with the time gate [-32,32] ms.
2.4.6. Choosing a steering algorithm
Different steering algorithms are available. The precise FFT algorithm yields an almost perfect steering cube at the costs of considerable longer calculation times. The BG Fast Steering algorithm seems to fit 95% of the situations, and is very fast. dGB recommends the use of the BG algorithm using its default calculation cube size of 3, and with additional median filtering 113. Though depending on geology, data quality, available computation time and purpose, other choices can be made. A number of examples are presented:
For a dataset of good quality with only small and low variance dips the BG Fast Steering method performs well enough. With a median filtering with stepout 1 1 3 applied, the result is already very acceptable.
For a poor quality dataset one of the FFT algorithms should be used, because the BG Fast Steering algorithm is sensitive to noise and will produce too much outliers. A small vertical filtering might also help to improve the results.
For detailed studies at target level one can consider to apply the precise FFT algorithm to a sub volume at the area of interest.
It is always possible to go back and spend much more time in producing a steering cube using the FFT precise algorithm.