2D Interferometric VSP->SSP Migration of VSP Multiples using Diffraction-Stack Migration





Figure 1. (a). The velocity model used to generate the synthetic VSP data. In this model, there are 100 shots evenly deployed on the surface, and 16 geophones evenly placed in the leftside well (offset 0 m) at the depth range from 490 m to 790 m. (b). A common receiver gather of the synthetic VSP data. (c). The interferometric migration image of the VSP multiples.

Objective: Learn how to implement the 2D interferometric migration method using the diffraction-stack migration method, and study the advantages of VSP multiple migration. This is an example of the 2D VSP->SSP correlation transform and the lab is written by Shuqian Dong.

Introduction: The interferometric migration of virtual SSP data (obtained by the VSP->SSP transform) use the VSP multiples to delineate the subsurface reflectivity distribution. Migration of virtual SSP data has several advantages. It is insensitive to receiver-related static errors in the well, and it has wider subsurface illumination than the migration of VSP primary reflections. The main disadvantage includes a strong acquisition footprint in the generation of virtual SSP traces, i.e., the virtual SSP Green's functions are incomplete due to the limited aperture of VSP receivers in the well. The implementation of VSP interferometric migration consists of the following steps.

  1. Separate the VSP data into upgoing and downgoing waves (The data provided in this lab are already separated. We do not need this part in this lab.).
  2. Obtain the direct waves and downgoing free-surface multiples by a proper muting/filtering process.
  3. Crosscorrelate the direct waves with multiples, and sum the correlated records over stations in the well to generate the crosscorrelated virtual SSP data.
  4. Migrate the virtual SSP data.

Procedure:

  1. Make a directory, and load in the file: The main program: main.m, diffraction migration codes:mig1.m, direct wave muting codes:mutedata.m, raytracing code:Mray.m.
  2. Load the velocity file vel.mat and the traveltime table traveltimesrc.mat. Load the synthetic VSP data set data.zip, and use command 'unzip data.zip' to extract the data into the same directory.
  3. Run program main.m in Matlab to redatum the VSP data to virtual SSP data and migrate the virtual SSP data (It costs about half an hour).
  4. Try to subsample the shot gathers and show the effects of sparsity on the migration image.
  5. 16 VSP common receiver gathers are used in this lab to generate the virtual SSP data set. Try to use only one VSP common receiver gather and repeat the test. Use stationary phase theorem to explain why we can not get a decent virtual SSP data but can get a fairly good migration image in this case.

Questions:

  1. What are the advantages and disadvantages of migrating VSP multiples compared to migrating VSP primaries?
  2. In practice, we can crosscorrelate the multiples with the whole traces instead of the direct waves to generate the crosscorrelogram. Why does this strategy work? Modify the code to generate crosscorrelogram by whole traces. Compare the migration images obtained by different strategies. Which one is better? Show a ray diagram that redatums VSP to SWP events to show why the latter strategy is more powerful.
  3. Research Topic: Use least squares migration (Nemeth et al., 1999, Least-squares migration of incomplete reflection data, Geophysics, 208-221.) to improve the migration result.
  4. Research Topic: Use LSD or least squares redatuming (Schuster and Zhou, 2006, A theoretical overview of model-based and correlation-based redatuming methods, Geophysics, SI103) to improve the redatumed SSP data. Now migrate this redatumed data and compare the resulting image to the least squares migration image. Comment about salient differences between the migration of LSD SSP data and least squares migration of the virtual SSP data.