The Application of Genetic Algorithm to CSAMT Inversion for Minimum Structure

We apply genetic algorithm to invert CSAMT data. Here, we invert both apparent resistivity and phase data which contain the near-field, transition zone field and the far-field without any correction. The genetic algorithm is one kind of global optimization methods with less dependence on the initial model and better ability to find most available resolutions. But in the case of many unknowns the multi-solution is still a problem. When we use a multi-layer model in inversion, CSAMT does not yield a unique solution. In order to reduce the temptation to over interpret the data and to eliminate arbitrary discontinuities in simple layered models, we employ the minimum structure to constrain the invert result. We have defined minimum structure function for the CSAMT inversion, which is based on the genetic algorithm, and have found the optimal value of the Lagrange multiplier µ = 0.5. The designed models are H, A, K, Q and HKH, KHA. When the data have no noise or have 10% noise, the invert resistivity models fit the true models well. When the data contain 20% noise, the invert result is also good. The method has been used for field data processing, the result was good. Both synthetic and field data examples indicate that the method is effective.