In this work we apply a Genetic Algorithm (GA) approach to the residual statics computation problem. This type of geophysical optimization problem is characterised by many local minima produced by the so-called cycle-skipping phenomenon. The application of a global optimization method is particularly suitable in this context as it is able to jump out from local minima where gradient-based methods can easily be entrapped. We use an analytical objective function to test the capability of GA in finding the global minimum in case of highly non-linear multi-minima objective function. Because the residual statics optimization problem involves many unknown model parameters, in this analytical test we are particularly interested in analysing the rate of convergence (that is the number of evaluated models required to reach the global minimum) as the dimension of the model space increases. We then show the use of this methodology on a field seismic reflection data set acquired for near surface investigations. The application of the residual statics derived by the GA method produces final CMP gathers with flatter reflectors and a final stack section in which the continuity of the observed events increases.


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