Due to the induced polarization (IP) effect, the sign reversal often occurs in time-domain airborne electromagnetic (AEM) data. The inversions that do not consider IP effect 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 difference 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 effectiveness 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.

由于感应极化（IP）的影响，符号反转通常发生在时域机载电磁（AEM）数据中。不考虑IP效应的反演无法恢复真正的地下电气结构。鉴于机载极化数据在时域中存在许多参数，且参数之间的灵敏度差异较大，这给反演的稳定性和准确性提出了挑战。本文提出了一种基于Pearson相关约束的具有IP效应的时域AEM数据反演方法。该方法在统计中使用Pearson相关系数来表征电阻率和可充电性之间的相关性，并构造Pearson相关约束条件以求取目标函数的反演，从而减少反演的非唯一性。为了验证该方法的有效性，我们对合成数据执行了Occam的反演和Pearson相关约束反演。实验表明，与Occam的反演相比，Pearson相关性约束的Inverison更加准确和稳定。最后，我们对具有和不具有IP效应的调查数据集进行了反演。结果表明，考虑IP效应后，数据的不匹配度和反转部分的连续性得到了极大的改善。我们对合成数据执行Occam的反演和Pearson相关约束反演。实验表明，与Occam的反演相比，Pearson相关性约束的Inverison更加准确和稳定。最后，我们对具有和不具有IP效应的调查数据集进行了反演。结果表明，考虑IP效应后，数据的不匹配度和反转部分的连续性得到了极大的改善。我们对合成数据执行Occam的反演和Pearson相关约束反演。实验表明，与Occam的反演相比，Pearson相关性约束的Inverison更加准确和稳定。最后，我们对具有和不具有IP效应的调查数据集进行了反演。结果表明，考虑IP效应后，数据的不匹配度和反转部分的连续性得到了极大的改善。