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Removing seafloor multiple using predictive deconvolution and NMO correction

Removing seafloor multiple using predictive deconvolution and NMO correction. Noppadol Poomvises Geologist 6. Ghost A short-path seismic pulse leaving source in upward direction, following and arriving at receivers closely with primary ( P ) signal.

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Removing seafloor multiple using predictive deconvolution and NMO correction

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  1. Removing seafloor multiple using predictive deconvolution and NMO correction Noppadol Poomvises Geologist 6

  2. Ghost A short-path seismic pulse leaving source in upward direction, following and arriving at receivers closely with primary (P) signal. Superimposition to primary and broadening seismic waveform. Seafloor multiple Seismic energy trapped between two strong interfaces of high reflection coefficient (R). Periodicity interval equals to 2-way travel time. Effect as wave-train reverberation in seismic stacked section. Air R=-1 Water SB =Primary reflection from seafloor M1=Twice-bounced multiple M2=Three-bounced Water R multiple Seafloor 0 P R M1 -R 2 R M2 3 M3 -R 4 (a) Seafloor multiple model (modified from Russel, 1993). Air R = -1 Water 10 m 0.013 s (b) Ghost model (modified from Jadell, 1987). Seismic suffering from undesirable noises.

  3. Objectives of the study • To evaluate the ability of multiple attenuation, both in modeled and real data, using conventional predictive deconvolution (PDC) in common shot domain using Focus/Disco 4.1, the in-house processing software • To examine the multiple removing technique by applying the periodicity enhancement and PDC in the common shot domain, using the same software. • To compare the quality of processed data using the methods in 1 with those obtained from 2 to demonstrate whether significant improvement is achieved by the proposed technique.

  4. Data collection • Modeled data • Generating modeled data on a two-layered and a three-layered models using a Forward modeling technique. • Using Osiris modeling s/w, Odegaard & Danneskoild-Samsoe, Denmark. • Running on a Unix-based computer, Sun Sparc 20, 128 MB RAM, and 20 GB hard disk. • Real data • Two real seismic data sets acquired on shallow seafloor of two different areas and times. • The Ist set contains fair degree of multiples while the 2nd set shows stronger degree of multiples.

  5. Concept of the removing technique.

  6. Data processing works. • Generating modeled data. • Verifying the data. • Processing modeled data. • Processing real data.

  7. Generating modeled data. Distance (m) 0 10 600 Water surface Distance (m) 1. Introducing earth models and parameters to the S/W. 2. Simulating the models. 3. Receiving model shots. 0 10 600 Water surface 10 Receiver array Source Receiver array 10 Water layer Depth (m) Layer 1 Source V1 = 1,441 m/s, D = 1.0 kg/m3 100 Vw = 1,500 m/s, Dw = 1.0 kg/m3 Sea bottom V2 = 1,800 m/s, D = 1.5 kg/m3 50 Layer 2 Vsb = 2,000 m/s, Dsb = 2.0 kg/m3 400 V3 = 5,250 m/s, D = 2.6 kg/m3 Layer 3

  8. Generating the modeled and calculated shots Distance (m) 0 10 600 Water surface Receiver array Source 10 Water layer Depth (m) Vw = 1,500 m/s, Dw = 1.0 kg/m3 50 Sea bottom Vsb = 2,000 m/s, Dsb = 2.0 kg/m3 (a) A two-layer model for numerical computation (a) A two-layer model for numerical computation (c) Possibilities of seismic events predicted from the two-layer input model. (d) A calculated shot computed from the two-layer input model.

  9. Data verification by comparison between the modeled and calculated shot of the two-layer case. • Both contains primary and multiple events. • Well agreement between the simulated and predicted seismic events.

  10. Data verification by comparison between the modeled and calculated shot of the three-layer case. • Both contains primary and multiple events. • Well agreement between the simulated and predicted seismic events.

  11. (2) (3) PDC Flow processing sequencesof modeled data in three cases.

  12. (b) Conventional PDC (c) Periodicity before PDC (a) No PDC Figure 4 Processing result of two-layer model shots in three different cases with their autocorrelation(middle), and semblance analysis (lower). Processing results of two-layer modeled data Changes

  13. (a) No PDC (b) Conventional PDC (c) Periodicity before PDC Figure 5 Processing result of three-layer model shots in three cases with their autocorrelation(middle), and semblance analysis (lower). Processing results of three-layer modeled data Changes

  14. Processing sequence of real data and parameters used. (The numbers in embraces are of the data set 2)

  15. Changes Periodicity before PDC Conventional PDC No PDC Figure 6 Processing result in a shot record of real data set 1 in three different cases (above) with their semblance analysis(below). Processing resultsof 2-D seismic dataset 1

  16. Figure 7 Normal stacked (NSTK) section of data set 1 with no predictive deconvolution (left) and its corresponding autocorrelation (right) The numbers labeled are used to with other cases. Normal stacked (NSTK) section with NO predictive deconvolution(PDC) of data set 1.

  17. Figure 8 Normal stacked (NSTK) section of data set 1 with conventional predictive deconvolution (left) and its corresponding autocorrelation (right). NSTK section with conventional PDC of data set 1.

  18. Figure 9 Normal stacked (NSTK) section of data set 1 with periodicity before predictive deconvolution (left) and its corresponding autocorrelation (right). NSTK section with periodicity enhancement and PDC of data set 1.

  19. NSTK of data set 1 in three cases NSTK with NO PDC NSTK with conv. PDC NSTK with periodicity enhancement and PDC

  20. NSTK with periodicity enhancement and PDC NSTK with No PDC NSTK with PDC Autocorrelations of real data set 1in three cases.

  21. Changes Periodicity before PDC No PDC Conventional PDC Processing results of 2-D seismic data set 2 Figure 10 Processing result in a shot record of real data set 2 in three different cases (above) with their semblance analysis (below).

  22. Figure 11Normal stacked (NSTK) section of data set 2 with no predictive deconvolution (left) and its corresponding autocorrelation (right). The numbers labeled are used to compared with other cases. Normal stacked (NSTK) section with NO predictive deconvolution(PDC) of data set 2.

  23. Figure 12Normal stacked (NSTK) section of data set 2 with conventional predictive deconvolution (left) and its corresponding autocorrelation (right). NSTK section with conventional PDC of data set 2.

  24. Figure 13Normal stacked (NSTK) section of data set 2 with periodicity before predictive deconvolution (left) and its corresponding autocorrelation (right). NSTK section with periodicity enhancement and PDC of data set 2.

  25. NSTK with periodicity enhancement and PDC NSTK with NO PDC NSTK with conv. PDC NSTK of data set 2 in three cases

  26. NSTK with periodicity enhancement and PDC NSTK with NO PDC NSTK with PDC Autocorrelation of data set 2in three cases.

  27. Conclusions 1. Conventional PDC in common shot domain can suppress some amount of seafloor multiples from seismic data, especially at near offset range. 2. Periodicity enhancement and PDC can remove much amount of seafloor multiples from both data, especially at middle- and far-offset range. 3. The new technique can comparatively removes the seafloor multiples from seismic data much amount than that of the conventional technique. 4. Performance of the new technique relatively gives better improvement of the quality, and enhances resolution of stacked section than of the conventional method as well.

  28. Recommendations • The package of NMO/PDC/DNMO consumes only a few CPU time than of the conventional one, therefore it is attractive to apply the method in an actual data processing work. • For better development of seafloor multiple removing technique, it is of interested to further study the effectiveness of this method in future by compiling the package in common shot domain with other existing removing techniques.

  29. PTTEP public Co., Ltd. Providing an excellence chance. Supporting hardware, software, and valuable seismic data used. CMU The place I really love and memorize. The place that giving so many things more than education. Dr.Chalermkiet Tongtaow Dr.Banjob Yodsombat Dr.Pisanu Wongpornchai Dr.Somchai Sri-israporn Mr.Montri Rawanchaikul Mr.Booncherd Kongwang For their advise, guidance, and unwavering standing by me during my time of researching. Acknowledgements.

  30. Million thanks ! For the good time on the Loy Krathong festival !!

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