Multi-component source equalization

Martin Karrenbach

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ABSTRACT

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. I am extending 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 have employed 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. Determining the exact radiation pattern is a more difficult task and requires an estimate of subsurface parameters, while the method shown here works without one having to know any medium properties. 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. The least squares problem is solved in the time domain using the conjugate gradient method. 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. The algorithm, although designed for multi-component data, is directly applicable to conventional single component data.