3D GSMP General Surface Multiple Prediction

Overview Library

Accurate data-driven identification and removal of complex multiples

3D GSMP general surface multiple prediction is a full-3D true-azimuth implementation of the surface-related multiple elimination (SRME) technique. It is used for accurately predicting complex multiples, including diffracted and scattered multiple energy.

This robust, flexible technology successfully overcomes the challenges of sparse, missing, or irregular field data to deliver superior results for all geophysical situations:

  • structural complexity
  • Coil Shooting single-vessel full-azimuth acquisition, wide-azimuth acquisition, or conventional towed-streamer surveys
  • land and ocean-bottom system surveys
  • conventional and single-sensor acquisition systems
  • any degree of acquisition irregularities, such as cable feathering, receiver line and station deviation, and source line and station offsets.

Minimal preprocessing is required because interpolation, regularization, and extrapolation are conducted with the 3D GSMP prediction algorithm. To produce a high-quality multiple model, 3D GSMP prediction realizes the multiples at true azimuth to ensure an accurate match with the multiples in the input data.

In areas with complex imaging challenges, 3D GSMP prediction preserves double bounces and other complex primary events for removal with complementary techniques, such as conventional or shifted-apex Radon demultiple. This approach enables correctly migrating these events by using imaging algorithms, including reverse time migration (RTM), for the best possible reservoir image.

Optimal input data selection

In areas where there are multiple overlapping vintages of data or large infill volumes, datasets can differ in their signal-to-noise ratio, offset distribution, and other characteristics. The 3D GSMP prediction workflow uses the highest-grade input data to refine modeling of multiples for multisurvey and 4D projects.

Related services and products

Request More Information

Accurately Predict Complex Multiples

3D GSMP general surface multiple predictionTrue-azimuth 3D GSMP imageA shot gather from a survey acquired with coil geometry.
PrevNextZoom1 of 3