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Progress Report on Separation of Multisource Data

Progress Report on Separation of Multisource Data. Xin Wang February 5, 2009. Outline. Motivation :. Separating multisource data in order to get single shot gathers. Methodology. Applying a combination of several local adaptive filters. Numerical Results.

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Progress Report on Separation of Multisource Data

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  1. Progress Report on Separation of Multisource Data Xin Wang February 5, 2009

  2. Outline • Motivation: Separating multisource data in order to get single shot gathers • Methodology Applying a combination of several local adaptive filters • Numerical Results Synthetic tests of 2, 5 and 10 sources on 2D SEG/EAGE model • Conclusions 01

  3. Motivation Goal: Shoot more times in a certain time to improve the efficiency of seismic survey, especially in wide azimuth angles surveys. CSG without time overlap 0 Constraint: Avoid time overlaps in seismic records from different sources. Time (s) Multisource CSG with time overlap Single CSG 1 Single CSG 2 0 0 0 8 Challenge: Can we separate multisource data with time overlaps to get single shot gathers? 1 Trace # 129 Time (s) Time (s) Time (s) 8 8 8 1 129 1 Trace # 129 Trace # 129 1 Trace # 02

  4. Outline • Motivation: Separation multisource data to get single shot gathers • Methodology Applying a combination of several local adaptive filters • Numerical Results Synthetic tests of 2, 5 and 10 sources on 2D SEG/EAGE model • Conclusions 03

  5. Methodology Local adaptive subtraction matching filtering Slant stacking and median filtering Random time shooting with known time delay (Chevron, 2008). Applying slant stacking and median filtering Original data Applying threshold mute filter Original data Original data Multisource CSG Multisource CRG 0 0 0 0 0 0 0 Resort data from CSGs to CRGs. Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Filtered CSG Filtered CRG Time (s) 0 0 Apply a combination of slant stacking, median filtering, threshold mute filtering and adaptive subtraction matching filtering. Multisource CSG 1 Multisource CSG 2 0 0 Time (s) Time (s) 8 8 1 1 1 1 1 Trace # 129 1 Trace # 129 Trace # Trace # Trace # 1 1 1 Trace # 1 9 9 9 9 Time (s) Time (s) 1 1 Trace # 10 Resort filtered data from CRGs to CSGs. 8 8 1 Trace # 45 1 Trace # 45 8 8 04 1 Trace # 1 Trace # 129 129

  6. Workflow Multisource data Resort CSG to CRG with known time shift Resort CSG to CRG with known time shift Far offset: Near offset: Apply slant stacking and median filter Apply threshold mute filter Apply slant stacking and median filter Apply local adaptive subtraction matching filter Apply local adaptive subtraction matching filter Resort CRG to CSG 05

  7. Outline • Motivation: Separation multisource data to get single shot gathers • Methodology Applying a combination of several local adaptive filters • Numerical Results Synthetic tests of 2, 5 and 10 sources on 2D SEG/EAGE model • Conclusions 06

  8. Numerical Results SEG/EAGE model 0 Model Size: 5.9 (km) * 1.4 (km) Receiver number: 129 Range of random shooting time : 0~4s Z (km) 1.4 0 X (km) 5.9 2 sources number of shots: 120, shot interval: 27.4 m, multisource interval: 2600 m 5 sources number of shots : 45, shot interval: 27.4 m, multisource interval: 1152 m 10 sources number of shots : 65, shot interval: 9.14 m, multisource interval: 585 m 07

  9. Numerical Results 2 simultaneous sources Multisource CSG Multisource CRG 0 0 Time (s) Time (s) 8 8 1 1 Trace # 129 Trace # 120 Filtered CSG Filtered CRG 0 0 Time (s) Time (s) 8 8 08 1 1 Trace # 129 Trace # 120

  10. Numerical Results 2 simultaneous sources Multisource CRG Filtered CRG 0 0 Time (s) Time (s) Near offset traces 8 8 1 Trace # 120 Trace # 1 120 Multisource CRG Filtered CRG 0 0 Far offset traces Time (s) Time (s) 8 8 09 Trace # 1 1 Trace # 120 120

  11. Numerical Results 5 simultaneous sources Multisource CRG Filtered CRG 0 0 Near offset traces Time (s) Time (s) 8 8 1 1 Trace # 45 Trace # 45 Multisource CRG Filtered CRG 0 0 Time (s) Time (s) Far offset traces 8 8 10 1 1 Trace # 45 Trace # 45

  12. Numerical Results 5 simultaneous sources Multisource CSG Filtered CSG Actual CSG 0 0 0 Time (s) Time (s) Time (s) 8 8 8 1 Trace # 1 1 129 Trace # Trace # 129 129 11

  13. Numerical Results 5 simultaneous sources Kirchhoff Migration Image KM image of simultaneous data KM image of filtered data KM image of actual data 0 0 0 Z (km) Z (km) Z (km) 1.4 1.4 0 X (km) 5,9 0 0 X (km) 5.9 X (km) 5.9 12

  14. Numerical Results 5 simultaneous sources Kirchhoff migration image only with far offset data KM of far offset multisource data KM of far offset filtered data KM of far offset actual data 45 shots, 1.23 km 40 Receivers , 1.82 km 40 Receivers , 1.82 km 45 shots, 1.23 km 0 0 0 0 Z (km) Z (km) Z (km) Z (km) 1.4 1.4 1.4 1.4 0 X (km) 5.9 0 X (km) 5.9 0 0 X (km) 5.9 X (km) 5.9 13

  15. Numerical Results 10 simultaneous sources Near offset traces: Multisource CRG Filtered CRG Actual CRG 0 0 0 Time (s) Time (s) Time (s) 8 8 8 1 Trace # 1 1 65 Trace # Trace # 65 65 14

  16. Numerical Results 10 simultaneous sources Applying 10 iterations of median filter and adaptive subtraction filter Far offset traces: Applying slant stacking and median filter Applying adaptive subtraction filter Applying threshold mute filter Multisource CRG 0 0 0 Time (s) Time (s) Time (s) 8 8 8 1 Trace # 1 65 Trace # 65 1 65 Trace # 15

  17. Outline • Motivation: Separation multisource data to get single shot gathers • Methodology Applying a combination of several local adaptive filters • Numerical Results Synthetic tests of 2, 5 and 10 sources on 2D SEG/EAGE model • Conclusions 16

  18. Conclusions • Separation of multisource data can greatly improve the efficiency • of seismic surveys, especially with wide azimuth angle. • By applying several filters, we can separate 2 and 5 multisource • data with acceptable results. • With increasing number of multisource, the effectiveness of • separation degrades. • The effectiveness of separation method degrades with • increasing offsets. More effort is needed for the far offset data. • Future Work: improving filters, more multisource, and realistic • field data. 17

  19. Acknowledgement We would like to thank the UTAM 2008 sponsors for their support. Thank You

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