Impedance Inversion With Adaptive Genetic Algorithm And Its Application In Antarctica Ice Sheet Exploration

The polar ice sheets have a significant impact on the global climate system,which directly controls the global eustacy.Under the megatrends of global warming,it is of great scientific significance to understand and predict the stability and evolutionary trends of the polar ice sheets.RES(Radio-echo sounding)is an effective tool to measure structure and distribution of the ice thickness,the subglacial relief,the isochronous layer of ice and so on.It has achieved great success and became a main stream tool in the study of polar ice sheets.At present,the characteristics of Ice sheet morphology,such as wave arrival time,speed and so on,are mainly obtained from the characteristics of Electromagnetic wave kinematics.However,it is a key and challenging area in RES frontier study and it has great scientific significance and practical value to use the dynamic characteristics of reflected waves,such as amplitude,phase,polarization characteristics and so on,to invert and identify change status of physical characteristics of crystal orientation fabric(COF)in ice sheets.In order to extract the wave impedance information of the isochronous layer of ice sheet from the RES data,this paper carried out the research on high frequency electromagnetic wave impedance inversion technology to obtain the dielectric constant,crystal orientation fabric characteristics and other microscopic parameters,and provide key physical parameters for the ice sheet research.The polar ice sheet research lacks the initial model provided by logging data compared with near-surface exploration.In order to avoid the shortage of relying on initial model in traditional inversion methods,in this paper,it tried to apply genetic algorithm to wave impedance inversion problem.But it will result in unstable convergence of inversion results if genetic algorithm applied directly to wave impedance inversion.In order to solve the above problems,this paper introduced adaptive mechanism and real encoding system to improve the reliability and astringency of genetic algorithm.At the same time,referred the method of seismic exploration,this study introduced the convolution model into the inversion of RES data.It adopted the convolution model to simplify the propagation rules of electromagnetic waves in the ice sheet.By comparing with the Finite Difference Time Domain(FDTD)method,it proved that the forward calculation based on the convolution model can greatly improve the efficiency of genetic algorithm.In addition,the introduction of the adaptive mechanism and the real encoding system can significantly improve the reliability and astringency of genetic algorithm.we used the GPRMax software to make a numerical modeling to a known stratigraphic modeling to obtain its echo data.Then adaptive genetic algorithm was used to invert the data.After that,statistical posterior probability density(PPD)method is used to analyze the recovery degree and convergence of the theoretical model,so as to prove the reliability of the adaptive genetic algorithm and its advantages in high-dimensional global optimization.Moreover,the adaptive evolution mechanism can effectively avoid the algorithm from falling into the local optimal in the optimization process.Also,the polarization uniformize process were applied to counter with the problem of the random polarization switch in the Radio-echo sounding data in this study.The processed data improved the stability of the ice bed reflection event and provided a good data basis for genetic algorithm inversion.In this study,the adaptive genetic algorithm was successfully applied to invert the wave impedance of the Antarctic ice sheet.The experimental results show that the polarization radar echo signals of the Antarctic ice sheet show obvious layered structure characteristics,which is consistent with the position of ice age to interglacial period revealed by the drilling results of deep ice core.Therefore,it proved the corresponding relationship between the evolution of anisotropic ice fabric and the cycle of interglacial period and the glacial period.