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A global optimization method incorporating a ray-tracing scheme is used to invert observations of shear-wave splitting from two near-offset VSPs recorded at the Conoco Borehole Test Facility, Kay County, Oklahoma. Inversion results suggest that the seismic anisotropy is due to a non-vertical fracture system. This interpretation is constrained by the VSP acquisition geometry for which two sources are employed along near diametrically opposite azimuths about the well heads. A correlation is noted between the time-delay variations between the fast and slow split shear waves and the sandstone formations.

We analyze the crosshole data from the Antrim Shale gas play at the MIT test site in the Michigan Basin. A crosshole seismic logging analysis has revealed the presence of strong transverse isotropy possibly due to the alignment of clay platelets along the bedding plane or sedimentary bedding, and strong continuity in the depth interval covered in this study.Ž . We find that the data are dominated by channel waves associated with low velocity zones waveguides identified in the sonic logs. Channel waves are characterized by their unique characteristics, such as dispersion and amplitude variation with depth. The channel-wave analysis has revealed the possible presence of azimuthal anisotropy at the top of the depth covered in this survey, which may be related to fracturing in the Antrim Shale formation. The data also show strong attenuation of S-waves characterized by very low frequency contents which may be due to viscoelastic properties of shaly sands or scattering by fractures. However, direct estimates of fracture parameters are not possible due to the limited data available. Finally, we have modeled the data with synthetic seismograms using a model with a 30% anisotropy. q

We imaged the Marmousi dataset using an efficient Kirchhoff prestack depth migration algorithm combined with a layer-stripping velocity analysis technique. The method relies on repeated common-offset test migrations to build a velocity model systematically from the top down. Results of these common-offset depth migrations are sorted into common image-point displays. By observing residual moveout as a function of offset in these displays, we determine veloeities and boundary locations for each layer. The assumed model is 'blocky' with constant layer velocities. Application of this method to the Marmousi data produced a good quality image except in the central region with its large-velocity-contrast fault blocks. Our data treatment for the EAEG workshop consisted of wavelet processing, followed by our layer-stripping depth migration/model building, and residual processing. Some additional improvement has been obtained by reprocessing after the workshop. Although a blocky model with constant-velocity layers is a highly idealized model, our success in applying it to this complex synthetic dataset makes us more comfortable in using this method to image datasets from the real world. lNTRODUCTION Our procedure for building velocity models for depth migration uses common-offset depth migration combined with a top-down layer-stripping velocity analysis technique. The technique utilizes a stabie and Jast (12 minutes epu time for the Marmousi prestack dataset on a CRAY X-MP) Kirchhoff depth migration algorithm, which allows many trial veloeities to be tested.We processed the Marmousi dataset in three main steps: (1) pre-processing to remove the seismie wavelet, (2) iterative depth migration and layer-stripping velocity analysis, and (3) postmigration processing inc1uding a residual moveout correction. The second step, namely, iterative construction of the migration velo city model, comprised the bulk of the effort, and will also receive most of the discussion in this report.Our velocity-depth models consist of constantvelocity layers with arbitrarily dipping boundaries. We construct such a model systematically, one layer at a time, from the top down. With the model determined to any given layer, common-offset test migrations are run for various (constant) veloeities below that layer. The common-offset results are sorted into common image-point gathers, which help determine the velocity and location of the next layer.This velo city analysis is quite sensitive. It is usabie whenever the image-point gathers are sufficiently coherent to display event moveout over an adequate range of offsets. However, poor data quality or structural complexity can cause the method to break down at sufficiently great depths.For the Marmousi dataset, near the east and west ends of the line, our systematic layer-stripping method was usabie (and yielded accurate veloci-139