scholarly journals Study on internal multiple elimination method on Land Seismic Data

2015 ◽  
Vol 2015 (1) ◽  
pp. 1-3
Author(s):  
Luqing Cao ◽  
Tianyue Hu
Keyword(s):  
Seismic Data ◽  
Elimination Method ◽  
Multiple Elimination

LONG-PERIOD SURFACE-RELATED MULTIPLE SUPPRESSION IN 2D MARINE SEISMIC DATA USING PREDICTIVE DECONVOLUTION AND COMBINATION OF SURFACE-RELATED MULTIPLE ELIMINATION AND PARABOLIC RADON FILTERING

2021 ◽  
Author(s):  
Pimpawee Sittipan ◽  
Pisanu Wongpornchai
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Petroleum Reservoirs ◽  
The United States ◽  
Multiple Reflections ◽  
Long Period ◽  
Predictive Deconvolution ◽  
Short Period ◽  
Marine Seismic ◽  
Multiple Elimination

Some of the important petroleum reservoirs accumulate beneath the seas and oceans. Marine seismic reflection method is the most efficient method and is widely used in the petroleum industry to map and interpret the potential of petroleum reservoirs. Multiple reflections are a particular problem in marine seismic reflection investigation, as they often obscure the target reflectors in seismic profiles. Multiple reflections can be categorized by considering the shallowest interface on which the bounces take place into two types: internal multiples and surface-related multiples. Besides, the multiples can be categorized on the interfaces where the bounces take place, a difference between long-period and short-period multiples can be considered. The long-period surface-related multiples on 2D marine seismic data of the East Coast of the United States-Southern Atlantic Margin were focused on this research. The seismic profile demonstrates the effectiveness of the results from predictive deconvolution and the combination of surface-related multiple eliminations (SRME) and parabolic Radon filtering. First, predictive deconvolution applied on conventional processing is the method of multiple suppression. The other, SRME is a model-based and data-driven surface-related multiple elimination method which does not need any assumptions. And the last, parabolic Radon filtering is a moveout-based method for residual multiple reflections based on velocity discrimination between primary and multiple reflections, thus velocity model and normal-moveout correction are required for this method. The predictive deconvolution is ineffective for long-period surface-related multiple removals. However, the combination of SRME and parabolic Radon filtering can attenuate almost long-period surface-related multiple reflections and provide a high-quality seismic images of marine seismic data.

3D surface-related multiple elimination using parabolic sparse inversion

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. V145-V152 ◽  
Author(s):  
Ketil Hokstad ◽  
Roger Sollie
Keyword(s):  
Seismic Data ◽  
Alternative Formulation ◽  
Practical Implementation ◽  
3D Seismic ◽  
Systems Of Equations ◽  
Production Scale ◽  
Seismic Acquisition ◽  
Sparse Inversion ◽  
Multiple Elimination ◽  
3D Seismic Data

The basic theory of surface-related multiple elimination (SRME) can be formulated easily for 3D seismic data. However, because standard 3D seismic acquisition geometries violate the requirements of the method, the practical implementation for 3D seismic data is far from trivial. A major problem is to perform the crossline-summation step of 3D SRME, which becomes aliased because of the large separation between receiver cables and between source lines. A solution to this problem, based on hyperbolic sparse inversion, has been presented previously. This method is an alternative to extensive interpolation and extrapolation of data. The hyperbolic sparse inversion is formulated in the time domain and leads to few, but large, systems of equations. In this paper, we propose an alternative formulation using parabolic sparse inversion based on an efficient weighted minimum-norm solution that can be computed in the angular frequency domain. The main advantage of the new method is numerical efficiency because solving many small systems of equations often is faster than solving a few big ones. The method is demonstrated on 3D synthetic and real data with reflected and diffracted multiples. Numerical results show that the proposed method gives improved results compared to 2D SRME. For typical seismic acquisition geometries, the numerical cost running on 50 processors is [Formula: see text] per output trace. This makes production-scale processing of 3D seismic data feasible on current Linux clusters.

Surface‐related multiple elimination on land seismic data—Strategies via case studies

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 719-734 ◽  
Author(s):  
Panos G. Kelamis ◽  
D. J. Verschuur
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Input Data ◽  
Multiple Attenuation ◽  
Processing Strategies ◽  
Processing Step ◽  
Subsequent Elimination ◽  
Seismic Amplitudes ◽  
Multiple Elimination

Three processing strategies for the estimation and subsequent elimination of surface‐related multiple energy on land seismic data are presented. They can be applied in a prestack mode (to shot and common‐midpoint gathers) or in a poststack mode. The algorithm for the multiple attenuation is based on wave theoretical principles in which the data are used as a prediction operator. The estimated multiples are then adaptively subtracted from the input data to obtain primary‐only data. A processing step prior to applying multiple elimination is an important component of these methodologies, particularly in the prestack analysis. Its aim is to regularize the data, improve the S/N ratio, and balance the seismic amplitudes. This results in smooth prediction operators. The effectiveness of these schemes in suppressing multiples is demonstrated with a number of case studies involving processing land seismic data.

Adaptive multiple subtraction based on multiband pattern coding

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. V69-V78 ◽  
Author(s):  
Jinlin Liu ◽  
Wenkai Lu
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Subtraction Problem ◽  
Frequency Bands ◽  
Main Challenge ◽  
Multiple Elimination ◽  
Pattern Coding

Adaptive multiple subtraction is the key step of surface-related multiple elimination methods. The main challenge of this technique resides in removing multiples without distorting primaries. We have developed a new pattern-based method for adaptive multiple subtraction with the consideration that primaries can be better protected if the multiples are differentiated from the primaries. Different from previously proposed methods, our method casts the adaptive multiple subtraction problem as a pattern coding and decoding process. We set out to learn distinguishable patterns from the predicted multiples before estimating the multiples contained in seismic data. Hence, in our method, we first carried out pattern coding of the predicted multiples to learn the special patterns of the multiples within different frequency bands. This coding process aims at exploiting the key patterns contained in the predicted multiples. The learned patterns are then used to decode (extract) the multiples contained in the seismic data, in which process those patterns that are similar to the learned patterns were identified and extracted. Because the learned patterns are obtained from the predicted multiples only, our method avoids the interferences of primaries in nature and shows an impressive capability for removing multiples without distorting the primaries. Our applications on synthetic and real data sets gave some promising results.

scholarly journals Practical Implementation of Multiple Attenuation Methods on 2D Deepwater Seismic Data : Seram Sea Case Study

2017 ◽  
Vol 32 (1) ◽  
Author(s):  
Tumpal Bernhard Nainggolan ◽  
Deny Setiady
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Complex Structure ◽  
Least Square ◽  
Inversion Method ◽  
Practical Implementation ◽  
Proper Solution ◽  
Multiple Attenuation ◽  
Multiple Elimination ◽  
Water Bottom

Some deepwater multiple attenuation processing methods have been developed in the past with partial success. The success of surface multiple attenuation relies on good water bottom reflections for most deepwater marine situations. It brings the bigger ability to build an accurate water bottom multiple prediction model. Major challenges on 2D deepwater seismic data processing especially such a geologically complex structure of Seram Sea, West Papua – Indonesia are to attenuate surface related multiple and to preserve the primary data. Many multiple attenuation methods have been developed to remove surface multiple on these seismic data including most common least-squares, prediction-error filtering and more advanced Radon transform.Predictive Deconvolution and Surface Related Multiple Elimination (SRME) method appears to be a proper solution, especially in complex structure where the above methods fail to distinguish interval velocity difference between primaries and multiples. It does not require any subsurface info as long as source signature and surface reflectivity are provided. SRME method consists of 3 major steps: SRME regularization, multiple modeling and least-square adaptive subtraction. Near offset regularization is needed to fill the gaps on near offset due to unrecorded near traces during the acquisition process. Then, isolating primaries from multiples using forward modeling. Inversion method by subtraction of input data with multiple models to a more attenuated multiple seismic section.Results on real 2D deepwater seismic data show that SRME method as the proper solution should be considered as one of the practical implementation steps in geologically complex structure and to give more accurate seismic imaging for the interpretation.Keywords : multiple attenuation, 2D deepwater seismic, Radon transform, Surface Related Multiple Elimination (SRME). Banyak metode atenuasi pengulangan ganda dikembangkan pada pengolahan data seismik dengan tingkat keberhasilan yang rendah pada masa lalu. Keberhasilan dalam atenuasi pengulangan ganda permukaan salah satunya bergantung pada hasil gelombang pantul pada batas dasar laut dan permukaan pada hampir seluruh survei seismik laut. Hal tersebut menentukan keakuratan dalam membuat model prediksi pengulangan ganda dasar laut dan permukaan air. Tantangan utama dalam pemrosesan data seismik 2D laut dalam khususnya struktur geologi kompleks seperti Laut Seram, Papua Barat – Indonesia adalah pada kegiatan menekan pengulangan ganda permukaan sekaligus mempertahankan data primer. Beberapa metode yang dikembangkan untuk menghilangkan pengulangan ganda permukaan pada data seismik seperti least-square, filter prediksi kesalahan dan transformasi Radon.Dekonvolusi Prediktif dan Metode Surface Related Multiple Elimination (SRME) digunakan sebagai solusi yang baik pada struktur kompleks dimana metode-metode lain gagal untuk memisahkan perbedaan kecepatan interval data primer dan pengulangan ganda. Metode tersebut tidak membutuhkan informasi bawah permukaan selain parameter sumber dan reflektivitas permukaan. Metode SRME terdiri dari 3 tahapan utama : regularisasi SRME, pemodelan pengulangan ganda dan pengurangan adaktif least-square. Regularisasi near offset diperlukan untuk mengisi kekosongan pada near offset yang disebabkan oleh adanya sejumlah tras terdekat yang tidak terekam selama akuisisi. Pemodelan maju digunakan untuk memisahkan data primer dan pengulangan ganda kemudian inversi dengan pengurangan input data dengan model multiple.Hasil pada data seismik 2D laut dalam menunjukkan bahwa metode SRME layak diterapkan sebagai salah satu pengembangan metode atenuasi multiple permukaan serta untuk meningkatkan akurasi data seismik terutama untuk struktur geologi kompleks.Kata kunci : peredaman pengulangan ganda (multiple), seismik 2D laut dalam, transformasi Radon, Surface Related Multiple Attenuation (SRME).

scholarly journals Seismic processing in the inverse data space

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. A29-A33 ◽  
Author(s):  
A. J. Berkhout
Keyword(s):  
Seismic Data ◽  
Dominant Role ◽  
Temporal Frequency ◽  
Data Matrix ◽  
Seismic Survey ◽  
Seismic Processing ◽  
Data Space ◽  
Matrix Formula ◽  
Multiple Elimination ◽  
Zero Time

Until now, seismic processing has been carried out by applying inverse filters in the forward data space. Because the acquired data of a seismic survey is always discrete, seismic measurements in the forward data space can be arranged conveniently in a data matrix [Formula: see text]. Each column in the data matrix represents one shot record. If we represent seismic data in the temporal frequency domain, then each matrix element consists of a complex-valued number. Considering the dominant role of multiple scattering in seismic data, it is proposed to replace data matrix [Formula: see text] by its inverse [Formula: see text] before starting seismic processing. Making use of the feedback model for seismic data, multiple scattered energy is mapped onto the zero time axis of the inverse data space. The practical consequence of this remarkable property may be significant: multiple elimination in the inverse data space simplifies to removing data at zero time only. Moving to the inverse data space may cause a fundamental change in the way we preprocess and image seismic data.

Adaptive surface‐related multiple elimination

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1166-1177 ◽  
Author(s):  
D. J. Verschuur ◽  
A. J. Berkhout ◽  
C. P. A. Wapenaar
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Simulated Data ◽  
Seismic Inversion ◽  
The Other ◽  
Inversion Process ◽  
Elimination Process ◽  
Free Data ◽  
Surface Reflectivity ◽  
Multiple Elimination

The major amount of multiple energy in seismic data is related to the large reflectivity of the surface. A method is proposed for the elimination of all surface‐related multiples by means of a process that removes the influence of the surface reflectivity from the data. An important property of the proposed multiple elimination process is that no knowledge of the subsurface is required. On the other hand, the source signature and the surface reflectivity do need to be provided. As a consequence, the proposed process has been implemented adaptively, meaning that multiple elimination is designed as an inversion process where the source and surface reflectivity properties are estimated and where the multiple‐free data equals the inversion residue. Results on simulated data and field data show that the proposed multiple elimination process should be considered as one of the key inversion steps in stepwise seismic inversion.

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