The above behavior is most noticeable in data recorded over low velocity media [Gulf of Mexico, offshore Nigeria etc.]. These data display a pronounced triangular onset of signal where the initial energy at the far offset does not arrive until more than halfway through the record. The ravn algorithm decimates the observed gain surface prior to the derivation of the robust fit resulting in too few samples from this zone being incorporated into process. In addition there is normally quite an expressed kink in the gain surface within this zone which when combined with the undersampling causes the ravn algorithm to class such data as noise to be ignored in the surface fit. To convince ravn that the affected data is indeed signal a significantly higher number of samples must be gleaned from this zone. This may be accomplished using the -top parameter which allows the user to specify a percentage of the record from which to use all samples in the calculation of the surface fit. Such oversampling allows the algorithm access to the higher amplitude information in affected zone but does not address its' pathologic triangular shape.
Recent work has advanced our understanding of the ravn algorithm, the effects of data preconditioning and the methodology needed to properly parameterize such datasets. This note will update the user to recent changes that have been made to the program as well as demonstrate an systematic parameterization procedure on a real dataset.
2. decimate the gain surface as specified by the command line entries -tstep [default = ntrc/12], -tstart[default =1], -tend [default=last trace], -sstep[default = 100ms] and -top [default = 20%].
3. calculate the polynomial coefficients describing the fit of order N, where N varies from 1 to the limit set by the command line parameter -ord [default = 0] to the agc gain surface. The default setting allows the program to determine when it has tested enough orders to have found the most statistically significant fit. The type of fit, least squares or robust, is controlled by the command line entry -f [default=robust] while the number of iterations used to compute the fit at each order is given by the command line entry -ilim [default=3]
4. calculate a regression coefficient and F-test value to determine statistical significance of the surface fit at this order.
5. repeat steps 3 and 4 over a program determined range of orders, or up to the limit set by -ord above.
6. determine which order tested provides the optimum, statistically significant fit with respect to the input data.
7. perform median trace adjustment if requested by the command line parameter -nomedian [default=used]. The number of iterations of median trace adjustment is given by the command line parameter -iter [default=3].
8. record the optimum surface order and coefficients along with any median adjustment coefficients in the attached coefficients file.
2. -median must now be included on the command line should you require median trace adjustment.
3. The default number of iterations for median trace adjustment, if requested, is now 0 [zero]. This means that if you flag ravn with -median you must also specify how many iterations of median trace adjustment you desire using the command line entry -iter.
4. The -tstep default has been set to 1[i.e.every trace].
5. The -sstep default is now 24 milliseconds.
6. A new command line entry, -sord [default=0] has been created which if specified will constrain ravn to fit only surfaces of the specified order for all records on the line. The default is still to have the program determine the most statistically significant order of fit.
Recent experimentation with several such datasets has yielded the observation that the stability of the surface fit can be improved if the data is preconditioned prior to running ravn. The following processing steps ought to be considered:
2. apply a normal moveout correction
3. apply a tn scalar [n = 1.0]
4. interpolate amplitudes into dead traces.
5. apply an onset mute combined with a low cut filter to remove excessive normal moveout stretch on the far offsets.
2. generate the median agc gain surface that ravn is trying to fit a robust surface to. Use the same window length and amplitude parameters and ask for a median agc using a median window increment of 10 milliseconds.
3. make a ravn pass on your data using the default parameters outputting the gain surface only. Examine the relationship between this gain surface and that generated in step 2 above. This surface should look like a robust fit to the former without any of the high frequency information.
4. if the above surface fit seems reasonable then accessing the coefficients file output by the previous run apply the gain surface to your data and examine the results. If you can see no obvious problems then you are ready to apply ravn to your whole dataset.
2. scrutinize the coefficients file and determine which order of fit corresponds to the most desirable results. Using the -sord parameter test this order against several others until you are satisfied with the order required to handle your data.
3. if instabilities persist or the fit is unsatisfactory test the -top parameter. Use the same procedure of observing the gain surface until you have determined the best value to use then apply it to your test records.
4. settle on the best -w parameter to use. A decrease in this parameter will decrease the program run time and heat up the scaled output. The optimum window length is decidedly data dependant.
5. once the best result is found from the above procedure, test the -ilim parameter for stability. If you notice very little change in the surface fit with an increase in this parameter then you are in good shape. If large or unexpected changes in the fit occur then your parameterization up to this point is suspect and should be reviewed. A good solution will converge nicely with increasing -ilim. If you cannot stabilize the surface fit by this point in the testing the input data must be ill-conditioned or you really do have large record to record variations in the decay surface [unlikely]. If you cannot do a better job of preconditioning the data then perhaps scaling this data with ravn is not appropriate and another type of scaling should be considered.
6. insert the -median flag on the command line and test the -iter parameter. This option is applicable if your input data displays trace to trace instabilities such as amplifier differences often seen on marine acquisition. Again a good solution will converge with increasing -iter value. Should unpredictable changes occur it is time to revisit a previous parameterization step or question the wisdom of ravn application on your dataset.
2. Case history of AVO effects done on real data with good geologic and engineering control.
The reason for the interpolation is to leave no holes in the decay surface sampled by ravn. If large gaps had been present within the shot records the interpolation process may not have been practical and the applicability of ravn would be in question. Once the surface fit coefficients have been derived for the line it is possible to apply them to the non-interpolated data should you so choose.
To minimize the effects of normal moveout stretch a low cut filter and a post nmo mute was applied. In addition a tn gain correction was performed to reduce the kink in the decay surface along the onset of data. This was accomplished using the following flow:
Frequently, depending on the color map and scaling used for viewing in xsd, a surface with seemingly good fit produces undesired amplitude variations when applied to the data. This pitfall can be avoided if one uses fixed scaling in xsd to display the data. To obtain the initial multiplier plot the median agc gain surface above using histogram scaling and adjust the percentage until you achieve an acceptable display. Record the scalar and offset applied to the display and use those values to display all gain surface results. Similarly use the scalar and offset determined from the first data result for all future data comparisons.
The resulting gain surface (Fig. 7a) is a demonstrably poor fit to the median agc surface (Fig. 6). A planar surface has been fit to the first 3 records and a fifth order surface fit to the last (Table 1). When applied to the data (Fig. 7b)The deep data is poorly scaled on the first three records while the inside offsets are heavily attenuated on the fourth.
| record | order |
|---|---|
| 126 | 1 |
| 151 | 1 |
| 176 | 1 |
| 201 | 5 |
| record | order |
|---|---|
| 126 | 7 |
| 151 | 5 |
| 176 | 5 |
| 201 | 5 |
Preconditioning of the dataset prior to parameterization and application of ravn, coupled with recent changes to the algorithm, particularly the specification of the surface order on the command line, has allowed the establishment of a methodology to address previously noted stability problems.
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