A comparison between non-linear optimizationmethods of Bayesian inversion and genetic algorithm forinverting spectral induced polarization data for Cole-Cole parameters

The induced polarization (IP) method has been used in mining prospecting andincreasingly in environmental investigations because IP measurements are verysensitive to the low frequency capacitive properties of rocks and soils. Cole-Colemodel parameters widely use to interpret both of time and frequency domaininduced polarization data.Among many studies in which Cole-Cole parameters are estimated from SIPmeasurements on soils and rocks, the majority use least squares methods. In thiswork, we developed a Bayesian method with simulated annealing samplingalgorithm to invert for double Cole-Cole parameters from SIP data. We alsoreproduced the genetic algorithm developed by Cao et al. and comparedperformance of simulated annealing method with genetic algorithm method throughinversion of synthetic data.Both of two methods are provides a global approach for inverting SIP data forCole-Cole parameters; the obtained estimates are independent of initial values. Ourresults show that for the SIP synthetic data with random noises up to ۱۰%, theinversed parameters obtained from simulated annealing method in comparison withgenetic algorithm method are more close to the real parameters

Keywords:

Bayesian inference , Simulated annealing , Genetic algorithm , Cole-Cole model , SIP , inversion.