- Copyright © 2003 Society of Exploration Geophysicists
In 3D seismic survey design, the parameter choice has to satisfy a wide variety of geophysical, operational, and cost constraints. The designer of a survey has to strike a balance between the various requirements and invariably will have to settle for some kind of compromise. Hence, this design process could also be viewed as an optimization process.
In an award-winning poster paper Liner et al. (1998) introduced the idea of survey design optimization. It was soon followed by another poster paper, but now in TLE (Liner et al., 1999). The method described in Liner, Underwood, and Gobeli (1999, to be called the LUG method in this paper) honors geophysical requirements only. This method determines design parameters based on the minimization of a cost function that is a weighted sum of deviations from the user-specified geophysical target values.
Morrice, Kenyon, and Beckett (2001) introduced a different survey design optimization method (to be called the MKB method). The MKB method focuses on the minimization of the actual cost of a 3D survey, while satisfying a number of geophysical constraints. The geophysical constraints can be formulated according to the same geophysical requirements as used in the LUG method. In the MKB method the cost function is minimized using the Solver function available in Microsoft Excel.
The MKB and LUG methods inspired this paper in which I will present modifications and improvements to both. The published methods deal with orthogonal geometry and so does this paper. To optimize the optimization method, a clear understanding of what constitutes a geophysically desirable configuration is essential. Therefore, this paper starts with a description of the main parameters of orthogonal geometry and their interrelationships. Next, various geophysical requirements that may influence the choice of the geometry parameters are reviewed. Then the LUG and the MKB methods are reviewed …