Application of True-Azimuth 3D SRME to an Onshore Mexican Data Set Simon Barnes*1, Roald van Borselen1, Humberto Salazar2, Alfredo Vàzquez2, Israel Ronzón2, Ruben Martinez1 1PGS; 2 Pemex Exploration and Production

Summary

A processing strategy for the 3D prediction and subsequent

elimination of long period surface-related multiples

(SRME) contaminating a 3D sparse non-orthogonal land

seismic data is presented. A comparison is made between

1D and 3D multiple prediction using the Surface-related

Multiple Elimination (SRME) method, showing that

significant improvements can be obtained by taking into

account the full 3D complexity of the subsurface. Multi-

dimensional Fourier regularization has been proven to be a

critical component of the pre-conditioning of the data

applied prior to the multiple prediction and subtraction.

Introduction

Multiple elimination remains one of the key challenges in

seismic data processing. Data-driven multiple prediction

schemes, such as Surface-related Multiple Elimination

(SRME), do not make use of any a priori information about

the subsurface geology. In principle, SRME is applicable

for both land and marine data. However, unlike marine

surveys, land data sets are characterized by variable near

surface conditions. In the surface multiple removal process,

the data itself is used as the multiple prediction operator.

Consequently, any contribution to the data that does not

represent upgoing body waves from the subsurface will

hamper the multiple prediction process. For land data, such

disturbing effects are the presence of direct waves and

surface waves such as ground roll. Other near surface

consistent filtering effects also have to be corrected for

appropriately. Furthermore, the intrinsic irregular sparse

spatial sampling of source and receivers commonly found

in onshore data acquisition needs to be accounted in order

to generate the densely populated data set that is required

for accurate 3D multiple prediction.

True-azimuth 3D multiple prediction

For 3D land data sets, much of the published work on

SRME has been restricted to utilization of 1D multiple

prediction schemes, applied in the common midpoint

domain (CMP) domain (Alá’i and Verschuur, 2006; al

Nahhas et al., 2008). To meet the appropriate spatial

Nyquist criteria for surface-related multiple prediction,

super CMP gathers are formed. If the lateral homogeneity

assumption of the multiple-generating overburden can be

relaxed, such one-dimensional approach may lead to

satisfactory results. However, similarly to marine

applications, even if the three dimensional nature of the

subsurface geology is modest, the best demultiple results

are obtained using a 3D prediction scheme that honours the

true multi-dimensional character of the data, and the true

azimuth of the original input data.

In 3D SRME, for each source and receiver pair in the data,

multiples at the free-surface are predicted through a

recursive convolution process of 3D common shot gathers

with 3D common receiver gathers (Verschuur and

Berkhout, 1997):

where f indicates frequency, and indicate

the receiver and source locations respectively, is the

best available estimate of the upgoing multiple-free

data, and equals the multiple-contaminated upgoing

pressure wavefield, as obtained from the recorded field

data. For land surveys, constitutes the data after the

removal of for the disturbing effects caused at the near

surface and after the reduction of acquisition-related noise.

For successful computation of the 3D multiples, the

sampling of these shot and receiver gathers must obey the

well-known Nyquist criteria. As data acquired on land are

typically under-sampled and irregular, a data regularization

and reconstruction approach is required to generate densely

sampled regularized 3D common shot and receiver gathers.

After the final data reconstruction step, an aperture around

each original input trace is defined, within which the

source- and receiver side traces are convolved (Figure 1).

The most appropriate traces to be used are found by

minimizing the differences in offset, midpoint position and

azimuth between the regularized traces and the desired

trace. After the “best fitting” traces are found, a correction

for the final reconstruction is applied to account for either

the differences in offset alone (van Dedem and Verschuur,

1998), or the differences in offset, midpoint and azimuth,

through the utilization of dip-dependent corrections (Aaron

et al., 2010).

Multi-dimensional Data Regularization

Using 2D and 3D regularization schemes, it is often

difficult to find suitable data domains to regularize the data.

For example, if data regularization is done in common

offset classes, than a large range of offsets may be needed

to obtain a sufficient dense sampling. The large variation in

offsets will lead to significant time shifts of events with

moveout. Although NMO correction can be used to reduce

the moveout, this will not work for both primaries and

multiples. Higher dimensional regularization uses all

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3D SRME to an Onshore Mexican Data Set

available data in one step, takes variations with offset into

account, and also benefits from a sparser transform domain.

The method used in this paper is based on the anti-leakage

Fourier transform (ALFT), as proposed by Xu et al. (2004

and 2005). The ALFT is a regularization method using an

iterative procedure for computing the spectrum of

irregularly sampled data. For each iteration, a discrete

Fourier transform is performed, the maximum Fourier

component selected followed by a transform back to the

irregular grid. Each time the component is subtracted from

the input data and the result used in the next iteration. As

discussed in Schonewille et al. (2009), for irregularly

sampled data, the ALFT can cope with very steep dips.

However, for regularly sampled data that are just as sparse,

the aliased Fourier components of a steeply dipping event

can have the same amplitude as the true component leading

to that event not being properly reconstructed. In practice

results from regularization can also be degraded for

situations where the sampling is (locally) close to regular.

To improve on the standard ALFT, an anti-aliasing method

is used where un-aliased lower frequencies provide spectral

weights for the higher frequencies. This helps avoiding

selecting the aliased component. In Schonewille et al.

(2009) it was shown on 2D synthetic and 3D field data that

the method can give a significant improvement for data

with steeply dipping events. This anti-aliasing ALFT has

been extended to 4D and 5D, and the 4D approach is the

technique that was used for the field data example shown in

this paper.

Like the 3D interpolation, the higher dimensional anti-

aliasing ALFT uses the exact locations of the input data.

While binning the locations to a regular grid could improve

efficiency (as it will be possible to use the FFT), it would

lead to errors as the traces are assumed to be at different

locations than their true locations. The method deployed

here can use all data (redundant offset rejection does not

have to be carried out) and preserves amplitudes.

Field data case study

For this paper, a 3D non-orthogonal land survey from

Mexico was selected, where the presence of thick

carbonates in the overburden produced significant long

period surface-related multiple contamination.

Arrivals that did not contribute to the multiples of body

waves were removed as well as applying appropriate

surface consistent scaling and deconvolution. Residual

statics and datum statics were also applied before the

regularization step. The latter were removed prior to the 3D

multiple prediction computation as the multiple prediction

operator assumes that both source, receiver and multiple

contribution stations are at their respective down hole (for

dynamite) or surface locations (Kelamis and Verschuur,

2000). As the survey had been sparsely acquired, the

Fourier regularization step generated (a) midpoint sampling

that was four times denser, from 30m x 30m bins to 15m x

15m bins; and, (b) a regularized offset spacing four times

smaller than the nominal input, from an offset plane

spacing of 300m to regularized offsets every 75m (see

Figure 2).

Only the nominally fully populated offset planes (13 in

total up to a maximum offset of around 4000m) were used

for the input to the regularization but the quality of the

result (Figure 3) was more than adequate for subsequent

multiple prediction step and the aperture required. In this

case the regularized data has a far higher signal-to-noise

ratio than the original input. As described earlier, the ALFT

is an iterative technique, and using more iterations would

reconstruct more of the noise/less coherent data, but in this

case a limited number of iterations and a cleaner result

were preferred.

The long period multiples for this case study are difficult to

suppress in the common image gather domain and need to

be removed prior to migration (Figure 4). Assuming the

multiple generating overburden does not locally vary

laterally, a 1D CMP-orientated SRME approach can be

applied (Figure 5) leading to modest multiple attenuation.

Through the utilization of a full 3D prediction scheme, the

true azimuth of the original input , as well as the modest 3D

characteristics of the sub-surface geology are taken into

account, leading to a superior demultiple result compared

to using 1D prediction scheme. Note the similarity

between the computed 3D Surface-related Multiple

Prediction (SRMP) model and the difference between the

original input and after adaptive subtraction (Figures 6b

and 6c). This demonstrates the accuracy of the multiple

prediction model as only very mild adaptive subtraction

filtering was needed to match the predicted multiples with

the recorded multiples. Note also that the 3D predicted

multiples (Figure 6b) contains less noise than the 1D-based

computed multiple model (Figure 5b). For consistency, the

adaptive subtraction for both approaches was performed in

the CMP domain.

Conclusions

The successful application of 3D surface-related multiple

prediction and subtraction has been demonstrated to a 3D

onshore Mexican data set. Even though the geological

complexity of the subsurface was relatively modest, the 3D

multiple prediction flow clearly outperformed a 1D

application, because the azimuth of the original input data

is honored and subtle 3D geological features were

accounted for properly. Multi-dimensional Fourier

regularization, as well as the removal of surface waves, and

accounting for statics have all been important prerequisites

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3D SRME to an Onshore Mexican Data Set

to the successful application of 3D SRME to this onshore

3D data set.

Acknowledgments

The authors would like to thank Pemex Exploration and

Production for allowing us to show the land data example.

Figure 1: Simplified SRME flow for land outlining the

regularization, reconstruction and computation of the multiple

model.

Figure 2: Fold of coverage of an input near offset plane of a land

survey before (top: mid offset 600m, offset planes spaced every

300m) and after Fourier regularization (bottom: regularized offset

600m, offsets now spaced every 75m). The input has been

regridded for display from 30m x 30m bins to the regularized

output grid of 15m x 15m bins. Blue denotes a fold of one. Note

that the extent of regularization is constrained to the vicinity of the

original survey input.

Figure 3: NMO stacks before (top) and after (middle) Fourier

regularization of midpoint and offset. The original bin spacing

after regularization is retained for display. The profiles in yellow

show the variation in CMP fold (up to 60). Respective FK analysis

corresponding to the blue squares (bottom) show a slightly

spatially band limited but stable result suitable for computing the

multiple model with the steep diffraction event clearly preserved.

Sections are displayed in two-way time over 2s. Total length of

line: 11km.

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3D SRME to an Onshore Mexican Data Set

Figure 4: Input NMO stack.

Figure 5a: After 1D SRME.

Figure 5b: NMO stack of 1D SRMP.

Figure 5c: Difference after 1D SRME.

Figures 4-6: Comparison of 1D and 3D SRME results for a land

data set from Mexico. Through the utilization of a full 3D

prediction scheme, the true azimuth of the original input , as well

as the modest 3D characteristics of the sub-surface geology are

taken into account, leading to a superior demultiple result

compared to using 1D prediction scheme. Note the similarity

between the 3D computed Surface-related Multiple Prediction

(SRMP) model and the difference between the original input and

after adaptive subtraction (Figures 6b and 6c). This demonstrates

the accuracy of the multiple prediction model as only very mild

adaptive subtraction filtering was needed to match the predicted

multiples with the recorded multiples. For consistency, the

adaptive subtraction for both approaches was performed in the

CMP domain. All sections are displayed in two-way time from 1s

to 5.5s. Total length of line: 18km.

Figure 6a: After TA 3D SRME.

Figure 6b: NMO stack of TA 3D SRMP.

Figure 6c: Difference after TA 3D SRME.

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EDITED REFERENCES

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SEG Technical Program Expanded Abstracts have been copy edited so that references provided with the online metadata for

each paper will achieve a high degree of linking to cited sources that appear on the Web.

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