The application of Genetic Algorithm to CSAMT inversion For minimum structure

摘要

The authors apply genetic algorithm to invert CSAMT data. Here, the authors invert both apparent resistivity and phase data which contain the near-field, transition zone field and the far-field without any correction. Genetic algorithm is one kind of global optimization method with less dependence on initial model and more ability to find the most available resolution, but when the unknown is too much then the multi-solution is still a problem. When they use 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, the authors employ minimum structural to constrain the invert result. The authors have defined minimum structure function for the CSAMT inversion,which base on genetic algorithm, and have found the optimal value of the Lagrange multiplier μ = 0.5.The designed models are HKH, KHA. When the data absence of noise, the invert resistivity models fit the true models well. When the data with 10% noise, the invert result also good. The method have used for field data processing, the result was good. Both synthetic and field data examples indicate that the method is effective.