Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints*
Man Kai-Feng 1, Yin Chang-Chun 1, Liu Yun-He 1, Ren Xiu-Yan 1, Sun Si-Yuan 2, Miao Jia-Jia 3, and Xiong Bin 4
1. College of Geo-Exploration Sciences and Technology, Jilin University, Changchun 130026, China.
2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China.
3. China Geological Equipment Research Institute co., Ltd, Beijing 100120, China.
4. College of Earth Sciences, Guilin University of Technology, Guilin 541006, China.
Abstract：
Due to the induced polarization (IP) effect, the sign reversal often occurs in timedomain airborne electromagnetic (AEM) data. The inversions that do not consider IP eff ect cannot recover the true umderground electrical structures. In view of the fact that there are many parameters of airborne induced polarization data in time domain, and the sensitivity diff erence between parameters is large, which brings challenges to the stability and accuracy of the inversion. In this paper, we propose an inversion mehtod for time-domain AEM data with IP effect based on the Pearson correlation constraints. This method uses the Pearson correlation coefficient in statistics to characterize the correlation between the resistivity and the chargeability and constructs the Pearson correlation constraints for inverting the objective function to reduce the non uniqueness of inversion. To verify the eff ectiveness of this method, we perform both Occam’s inversion and Pearson correlation constrained inversion on the synthetic data. The experiments show that the Pearson correlation constrained inverison is more accurate and stable than the Occam’s inversion. Finally, we carried out the inversion to a survey dataset with and without IP effect. The results show that the data misfit and the continuity of the inverted section are greatly improved when the IP effect is considered.
MAN Kai-Feng,YIN Chang-Chun,LIU Yun-He et al. Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints*[J]. APPLIED GEOPHYSICS, 2020, 17(4): 589-600.
2020, Vol. 17 
