Statistical Sampling Enabled Full-Waveform Inversion

R.T. Coates¹, K. Jiao, W. Huang¹, A. Schiemenz¹, and D. Vigh¹

Abstract

Full-waveform Inversion has recently emerged as a promising method for refining seismic velocity models to achieve enhanced imaging. The algorithm involves iteratively updating the velocity model to improve the match between the recorded seismic data and the simulated waveforms, with the goal of estimating the true velocity structure. Each iteration typically requires multiple wavefield extrapolations. As a result the technique places significant computational burdens on even the largest computers when applied to commercial three-dimensional surface seismic datasets.

This computational cost has been attacked previously by combining the processing of multiple physical shots into a single ‘encoded-shot’, using random encoding techniques (Krebs et al, 2009). The encoding can be based upon time shifts, polarity reversal or convolution with a short random series, any of which may be changed between iterations. While this technique works well for geometries with fixed receiver arrays (e.g. ocean-bottom cables) additional steps are usually required when applied to moving arrays both because the area occupied by the encoded shot grows in comparison to a single shot, and because not every receiver registers data from every shot in the recorded data.

AAPG Search and Discovery Article #90188 ©GEO-2014, 11th Middle East Geosciences Conference and Exhibition, 10-12 March 2014, Manama, Bahrain