Super-virtual Interferometric Diffractions as Guide Stars Wei Dai 1, Tong Fei 2, Yi Luo 2 and Gerard T. Schuster 1 1 KAUST 2 Saudi Aramco Feb 9, 2012

Outline Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary

Introduction Diffracted energy contains valuable information about the subsurface structure. Goal: extract diffractions from seismic data and enhance its SNR.

Previous Work Reciprocity equation of correlation and convolution types (Wapenaar et al., 2004). Diffracted waves detection (Landa et al., 1987) Diffraction imaging (Khaidukov et al., 2004;Vermeulen et al., 2006; Taner et al., 2006; etc)

Flip Guide Stars

Outline Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary

Step 1: Virtual Diffraction Moveout + Stacking y zw3 dt w2w1 y z y’ dt y z y’ = Benefit: SNR = N

Step 2: Dedatum virtual diffraction to known surface position y z y’ y zx x = * Convolution to restore diffractions y zx = y’ y zx *

Stacking Over Geophone Location x z Desired shot/ receiver combination Common raypaths Benefit: SNR = N

Super-virtual Diffraction Algorithm = w z 1. Crosscorrelate and stack to generate virtual diffractions w z Virtual src excited at -t zz’ z’ Benefit: SNR = N = * 2. Convolve and stack to generate super-virtual diffractions w z z

Workflow Raw data Select a master trace Cross-correlate to generate virtual diffractions Repeat for all the shots and stack the result to give virtual diffractions Convolve the virtual diffractions with the master trace Stack to generate Super- virtual Diffractions dt = = *

Outline Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary

Synthetic Results: Fault Model 0X (km)6 0 Z (km) km/s

Synthetic Shot Gather: Fault Model 0Offset (km)2 0 Time (s) 3 Diffraction Shot at Offset 0.2 km

Synthetic Shot Gather: Fault Model 0.5 Time (s) 1.5 Windowed Data 0Offset (km)2 0.5 Time (s) 1.5 Median Filter 0 Offset (km) 2 Our Method 0.5 Time (s) 1.5 0X (km)6 0 Z (km) 3

Estimation of Statics 0 Offset (km)2 0.5 Time (s) 1.0 Picked Traveltimes Predicted Traveltimes Estimate statics

Outline Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary

Experimental Cross-well Data 0 Depth (m) Time (s) Depth (m) Time (s) 0.9 Picked Moveout 0.6 Time (s) Depth (m) 280

Experimental Cross-well Data 180 Depth (m) Time (s) Depth (m) Time (s) 0.9 Median Filter Time Windowed 180 Depth (m) 0.6 Time (s) Super-virtual Diffractions

Experimental Cross-well Data 0 Depth (m) Time (s) Depth (m) Time (s) 0.9 Super-virtual Diffraction 0.6 Time (s) 0.9 Median Filtered 180 Depth (m) 280

Diffraction Waveform Modeling Born Modeling 0Distance (km)3.8 0 Depth (km) Depth (km) Time (s) Distance (km) 3.8 Velocity Reflectivity

Diffraction Waveform Inversion 0Distance (km)3.8 0 Depth (km) Depth (km) 1.2 Initial Velocity Estimated Reflectivity 0 Depth (km) 1.2 Inverted Velocity 0Distance (km)3.8 0 Depth (km) 1.2 True Velocity

Outline Introduction Super-virtual stacking theory Synthetic data examples Field data examples Summary

Super-virtual diffraction algorithm can greatly improve the SNR of diffracted waves.. Limitation Dependence on median filtering when there are other coherent events. Wavelet is distorted (solution: deconvolution or match filter).

Acknowledgments We thank the sponsors of CSIM consortium for their financial support.