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Extended Internal Multiple Prediction (XIMP)

Overview Library

Interbed multiples usually arise through seismic reflections bouncing between closely spaced geological beds, resulting in multiples that have little, if any, velocity or moveout discrimination from the primaries that generated them. Extended internal multiple prediction, XIMP, is a data-driven demultiple algorithm from WesternGeco with a minimum requirement for a priori information. The method is an extension of surface multiple prediction and is suitable for all acquisition geometries.

Interbed multiples, mainly contaminating land datasets, have been more difficult to address than surface-related multiples. These multiples usually appear very similar to the primaries because of their comparable velocities; they are therefore difficult to differentiate especially in areas with relatively flat geology.

XIMP is able to handle any acquisition geometry and predicts multiples at true azimuth. This is particularly important for rich-azimuth land acquisition. XIMP provides a true-azimuth, interbed multiple model and an accurate estimate of the multiple energy in the data. The multiple energy can then be removed from the data using adaptive subtraction techniques. When used in conjunction with 3D surface demultiple techniques the result is a much better representation of the primary energy.

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At a Glance

  • A data-driven internal multiple prediction algorithm with a minimum requirement for a priori information
  • Relaxes the periodicity and velocity discrimination requirements of regular demultiple
  • Works on any acquisition geometry and predicts multiples at true azimuth
  • Accurately predicts complex multiples.
  • Produces an accurate estimate of the multiple energy in the data
  • Multiples are removed using adaptive subtraction techniques
  • Does not require shot interpolation, regularization, or near offset reconstruction.

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Poststack time migration (PSTM) Example gathersExample semblances Well-tie before and after interbed multiple attenuation
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