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

Seismic suffering from undesirable noises. Air Water 0.013 s Seafloor SB =Primary reflection from seafloor M1=Twice-bounced multiple M2=Three-bounced R -R 2 4 3 R=-1 P M1 M2 M3 (a) Seafloor multiple model (modified from Russel, 1993). (b) Ghost model (modified from Jadell, 1987). 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. 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.

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.

Data collection Modeled data Real 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.

Concept of the removing technique.

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

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

Generating the modeled and calculated shots Distance (m) 10 Water surface 600 Source Receiver array 10 Water layer Depth (m) Vw = 1,500 m/s, Dw = 1.0 kg/m3 50 Vsb = 2,000 m/s, Dsb = 2.0 kg/m3 Sea bottom (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.

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.

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.

Flow processing sequences of modeled data in three cases. PDC (2) (3)

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

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

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

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

Normal stacked (NSTK) section with NO predictive deconvolution(PDC) of data set 1. 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.

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

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

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

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

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

Normal stacked (NSTK) section with NO predictive deconvolution(PDC) of data set 2. Figure 11 Normal 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.

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

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

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

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

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.

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.

Acknowledgements. Dr.Chalermkiet Tongtaow Dr.Banjob Yodsombat 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.

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