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SUMMARY

Source equalization is necessary when source behavior changes with location within a given survey. Conventional robust methods characterize source differences as pure amplitude variations, assuming identical source time functions. Here I extend this idea to vector wave fields, with the aim of preparing the data to be useful in elastic parameter determination (not just imaging). The method I employ estimates source-location and source-component-consistent differences in the data. Those differences are parameterized as short 1-D filters in time and consequently correct the data to an average isotropic response. This is a meant to be a step toward the goal of determining absolute radiation patterns. The source equalization solves a least-squares problem in a way that is consistent in regard to source location and source component without trying to shape the wavelet as done in surface-consistent deconvolution. Results from a 9-component land data set show that this procedure equalizes the data mostly by doing differential rotations and slight modifications of the source time function. Multi-component source equalization is especially interesting in regard to diminishing biases introduced in inversion results and AVO, but even effects such as uneven stacks can be caused by unequal radiation patterns. I seek to equalize radiation patterns within a given survey in a component-consistent and source-location-consistent manner [][]. Such an equalization can be carried out in various ways. In this thesis I am merely considering equalization to quasi-isotropic patterns by using no subsurface information whatsoever. This equalization technique has the advantage of being purely a preprocessing step. The filtering technique is closely related to prediction error filtering, but should not be confused with surface consistent deconvolution [].