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Joint inversion of full-waveform ground-penetrating radar and electrical resistivity data: Part 1
Geophysics ( IF 3.3 ) Pub Date : 2020-11-06 , DOI: 10.1190/geo2019-0754.1
diego domenzain 1 , John Bradford 2 , Jodi Mead 1
Affiliation  

We have developed an algorithm for joint inversion of full-waveform ground-penetrating radar (GPR) and electrical resistivity (ER) data. The GPR data are sensitive to electrical permittivity through reflectivity and velocity, and electrical conductivity through reflectivity and attenuation. The ER data are directly sensitive to the electrical conductivity. The two types of data are inherently linked through Maxwell’s equations, and we jointly invert them. Our results show that the two types of data work cooperatively to effectively regularize each other while honoring the physics of the geophysical methods. We first compute sensitivity updates separately for the GPR and ER data using the adjoint method, and then we sum these updates to account for both types of sensitivities. The sensitivities are added with the paradigm of letting both data types always contribute to our inversion in proportion to how well their respective objective functions are being resolved in each iteration. Our algorithm makes no assumptions of the subsurface geometry nor the structural similarities between the parameters with the caveat of needing a good initial model. We find that our joint inversion outperforms the GPR and ER separate inversions, and we determine that GPR effectively supports ER in regions of low conductivity, whereas ER supports GPR in regions with strong attenuation.

中文翻译:

全波形探地雷达和电阻率数据的联合反演:第1部分

我们已经开发了一种用于对全波形探地雷达(GPR)和电阻率(ER)数据进行联合反演的算法。GPR数据通过反射率和速度对电容率敏感,通过反射率和衰减对电导率敏感。ER数据对电导率直接敏感。这两种类型的数据通过麦克斯韦方程组固有地联系在一起,我们共同对它们进行求逆。我们的结果表明,在尊重地球物理方法的物理特性的同时,两种类型的数据可以协同工作以有效地相互规范。我们首先使用伴随方法分别为GPR和ER数据计算灵敏度更新,然后将这些更新求和以说明两种类型的灵敏度。敏感性增加了范式,让这两种数据类型总是对我们的求逆贡献很大,这与它们各自的目标函数在每次迭代中得到的解决程度成正比。我们的算法没有对地下几何结构进行假设,也没有对参数之间的结构相似性做出任何假设,但需要注意一个良好的初始模型。我们发现我们的联合反演优于GPR和ER单独的反演,并且我们确定GPR在电导率低的区域中有效地支持ER,而ER在衰减大的区域中支持GPR。我们的算法没有对地下几何结构进行假设,也没有对参数之间的结构相似性做出任何假设,但需要注意一个良好的初始模型。我们发现我们的联合反演优于GPR和ER单独的反演,并且我们确定GPR在电导率低的区域中有效地支持ER,而ER在衰减大的区域中支持GPR。我们的算法没有对地下几何结构进行假设,也没有对参数之间的结构相似性做出任何假设,但需要注意一个良好的初始模型。我们发现我们的联合反演优于GPR和ER单独的反演,并且我们确定GPR在电导率低的区域中有效地支持ER,而ER在衰减大的区域中支持GPR。
更新日期:2020-11-12
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