Abstract
The ultimate goal in survey design is to obtain the acquisition parameters that allow to acquire the most affordable data that fulfills certain image quality requirements. We propose a method that allows to optimize the receiver geometry for a fixed source distribution, e.g., think of ocean bottom nodes. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiver density function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging. Currently, we work on extending our method for the source distribution.
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