殷长春，男，1965年生，教授，国家"千人计划"特聘专家，主要从事电磁勘探理论，特别是航空和海洋电磁方面的研究.E-mail：yinchangchun@jlu.edu.cn.

中图分类号: 传统的梯度反演方法已经广泛应用于频率域航空电磁数据处理中，然而此类方法受初始模型影响较大，且容易陷入局部极小.为解决这一问题，本文采用改进的变维数贝叶斯反演方法实现航空电磁数据反演.该方法根据建议分布对反演模型进行随机采样，并依据接受概率筛选合理的候选模型，最终获得反演模型的概率分布和不确定度信息.为解决贝叶斯反演方法对深部低阻层反演效果不佳的问题，本文通过引入合理加权系数，调整对反演模型约束强度，在很大程度上改善了反演效果.通过对模型统计方法进行改进，在遵循原有模型采样方法和接受标准的基础上，将满足数据拟合要求的模型纳入统计范围，削弱不合理模型对统计结果的干扰.本文最后通过对含有高斯噪声的理论数据和实测数据进行反演，并与Occam反演结果进行对比，验证了该方法的有效性. 变维数贝叶斯反演 Abstract: Traditional gradient inversion has been widely used in airborne EM data processing. However, the inversions are seriously affected by the starting model and can be easily trapped in local minima. To solve these problems, we apply the trans-dimensional Bayesian inversion in our airborne EM data inversion. In trans-dimensional Bayesian inversion, the candidate models are randomly sampled from the proposed distribution and screened by the probability of acceptance. The probability distribution for the inversion models are finally obtained that contains parameter uncertainties. We further introduce the weighting coefficients to adjust the constraints on inverse models, so that the inversions for deep conductive layer are largely improved. In addition, based on the original sampling and accepting criteria, by improving the statistic method, in which only those models that fit the data are brought into statistics, the disturbances from unreasonable models are weakened. Finally, we prove the validity of the method by contrasting the inversion results of the synthetic data contaminated with gauss noise and field data with Occam method. Key words: Airborne EM Frequency-domain Forward modeling Weights Trans-dimensional Bayesian inversion