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李赓,曹飞翔. 基于深度学习的二维航空大地电磁数据反演[J]. 科学技术与工程, 2021, 21(4): 1272-1278.
ligeng,caofeixiang.The Inversion Research For Two-Dimensional Airborne Magnetotelluric Data Based On Deep Learning[J].Science Technology and Engineering,2021,21(4):1272-1278.
基于深度学习的二维航空大地电磁数据反演
The Inversion Research For Two-Dimensional Airborne Magnetotelluric Data Based On Deep Learning
投稿时间:2020-04-22  修订日期:2020-11-03
DOI:
中文关键词:  航空大地电磁法  深度学习  卷积神经网络  倾子
英文关键词:Airborne magnetotelluric method  Deep learning  Convolutional neural network  Tipper
基金项目:国家重点基础研究发展计划(973计划)
     
作者单位
李赓 河南理工大学
曹飞翔 河南理工大学
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中文摘要:
      航空大地电磁法以其机动灵活、效率高等优点,适用于在地势崎岖的偏远环境中开展快速普查作业。目前,针对二维航空大地电磁数据的反演研究,大多采用对目标函数求解偏导数的方式进行,这会造成反演结果对初始模型依赖高,且极易陷入局部极小值。为解决上述问题,本文首先从麦克斯韦频率域方程组出发,推导了倾子响应计算公式。其次,采用有限单元法计算异常体地电模型倾子响应,并结合地下电阻率值标签,构建了深度学习样本数据集,采用卷积神经网络顺利实现了二维航空大地电磁数据反演。最后,针对不同埋深的二维异常体地电模型,分别采用传统电磁反演和深度学习反演方法进行反演研究,对比分析了不同反演方法反演结果。最终结果表明:相较于传统电磁反演方法,采用深度学习反演方法进行二维航空电磁数据反演,反演速度更快,准确度更高。
英文摘要:
      With consideration of advantages of airborne magnetotelluric method in flexibility and high efficiency, it is suitable for rapid reconnaissance in remote environments with rugged terrain. At present, the method of solving partial derivatives of the objective function was adopted in most inversion studies of two-dimensional airborne magnetotelluric data, which made the inversion results highly dependent on the initial model and easily fell into the local minimum value. In order to solve the above problems, firstly, this paper deduced the formula for calculating the tipper response from Maxwell's frequency domain equations. Secondly, the tipper response of geoelectric model of the anomalous body which were calculated by finite element method, combined with the label of the underground resistivity value and the deep learning sample data set was constructed. Finally, the method of traditional electromagnetic and deep learning inversion were used, to carry out the inversion research on the geoelectric models of two-dimensional abnormal bodies with different buried depths. The inversion results of different inversion methods were compared and analyzed. The final results show that the deep learning inversion method is faster and more accurate for two-dimensional airborne electromagnetic data inversion, compared with the traditional electromagnetic inversion method.
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