时间域航空电磁激发极化参数三维反演研究  

3D inversion of time-domain airborne EM data for IP parameters

作　　者：满开峰 殷长春[2] 刘云鹤[2] 孙思源 熊彬[5] MAN KaiFeng;YIN ChangChun;LIU YunHe;SUN SiYuan;XIONG Bin(Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China;Development Research Center for Natural Resource and Real Estate Assessment,Shenzhen(Center for Environmental Monitoring of Geology,Shenzhen),Shenzhen 518040,China;Aero Geophysical Survey and Remote Sensing Center for Natural Resources,Beijing 100083,China;College of Earth Sciences,Guilin University of Technology,Guilin 541006,China)

机构地区：[1]中国科学院深圳先进技术研究院,深圳518055 [2]吉林大学地球探测科学与技术学院,长春130026 [3]深圳市自然资源和不动产评估发展研究中心(深圳市地质环境监测中心),深圳518040 [4]中国自然资源航空物探遥感中心,北京100083 [5]桂林理工大学地球科学学院,桂林541006

出　　处：《地球物理学报》2023年第3期1269-1280,共12页Chinese Journal of Geophysics

基　　金：国家重点研发计划项目(2021YFB3202104);国家自然科学基金项目(42030806)联合资助。

摘　　要：时间域航空电磁中心回线(或重叠回线)装置晚期道数据受激电效应影响常出现符号反转现象.这类数据与多个激电参数相关,并且各参数之间灵敏度差异较大,导致反演存在严重的非唯一性.本文提出一种基于Pearson相关性约束和深度学习算法相结合的时间域航空电磁激发极化参数反演策略.该反演策略首先基于深度学习预测时间域航空电磁激电参数,进而给时间常数和频率相关系数一个较小的约束范围后再反演电阻率和极化率,由此大大减少反演的多解性.针对电阻率和极化率的反演,我们采用统计学中Pearson相关系数构建两种物性参数的相关性约束,进一步减少反演多解性.为验证反演策略的有效性,我们对双棱柱模型和拱形模型分别进行反演试算.理论测试结果表明,基于Pearson相关性约束的电阻率和极化率的反演结果比传统的高斯-牛顿反演结果更接近真实模型,而基于深度学习预测时间常数和频率相关系数后的电阻率和极化率反演结果与给定真实时间常数和频率相关系数后的反演结果效果相当.最后,我们对来自澳大利亚的带激电效应的航空电磁实测数据在考虑和不考虑激电效应条件下进行反演,结果表明考虑激电效应的反演无论数据拟合还是地电断面的连续性均得到明显改善.Due to the Induced Polarization(IP) effect, the sign reversal occurs frequently in the late-time channels of Airborne Electromagnetic(AEM) signal for central loop configuration. Since the EM signal is related to multiple IP parameters, and the sensitivity of each parameter varies, serious non-uniqueness can be observed when inverting the data. In this paper, we present an AEM inversion scheme in time-domain for IP parameters based on Pearson correlation constraint and deep-learning algorithm. The inversion first predicts the IP parameters in time-domain based on deep learning. After that, it gives a small range of constraints on the time constant and frequency exponent and then inverts the resistivity and chargeability. This can largely reduce the uniqueness of the solution. For the inversion of resistivity and chargeability, we use the Pearson correlation coefficients in statistics to construct the constraints of these two physical parameters, so that we can further reduce the non-uniqueness of solutions. To verify the effectiveness of our inversion scheme, we carry out the experiments on the synthetic models of double prisms or an arch. It is shown that the inversion results of resistivity and chargeability based on the Pearson correlation constraint are closer to the true model than the traditional Gaussian Newton method, the inverted resistivity and chargeability based on the predicted time constant and frequency exponent by deep learning are equivalent to those when their true values are given in the inversions. Finally, we invert an AEM survey dataset from Australia with and without IP effect, respectively. The results show that the data fitting and the continuity of geoelectrical section are both largely improved when the IP effect is considered.

关 键 词：时间域航空电磁 激发极化效应 3D反演 深度学习 Pearson相关约束 

分 类 号：P631[天文地球—地质矿产勘探]