Full-waveform Modeling and Joint Inversion of Electromagnetic Induction and Ground Penetrating Radar Data

Author(s):  
D. Moghadas ◽  
F. André ◽  
H. Vereecken ◽  
S. Lambot
2015 ◽  
Vol 26 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Jan van der Kruk ◽  
Nils Gueting ◽  
Anja Klotzsche ◽  
Guowei He ◽  
Sebastian Rudolph ◽  
...  

Geophysics ◽  
2021 ◽  
pp. 1-77
Author(s):  
diego domenzain ◽  
John Bradford ◽  
Jodi Mead

We exploit the different but complementary data sensitivities of ground penetrating radar (GPR) and electrical resistivity (ER) by applying a multi-physics, multi-parameter, simultaneous 2.5D joint inversion without invoking petrophysical relationships. Our method joins full-waveform inversion (FWI) GPR with adjoint derived ER sensitivities on the same computational domain. We incorporate a stable source estimation routine into the FWI-GPR.We apply our method in a controlled alluvial aquifer using only surface acquired data. The site exhibits a shallow groundwater boundary and unconsolidated heterogeneous alluvial deposits. We compare our recovered parameters to individual FWI-GPR and ER results, and to log measurements of capacitive conductivity and neutron-derived porosity. Our joint inversion provides a more representative depiction of subsurface structures because it incorporates multiple intrinsic parameters, and it is therefore superior to an interpretation based on log data, FWI-GPR, or ER alone.


Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. H97-H113 ◽  
Author(s):  
Diego Domenzain ◽  
John Bradford ◽  
Jodi Mead

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.


2010 ◽  
Vol 8 (6) ◽  
pp. 635-649 ◽  
Author(s):  
Anja Klotzsche ◽  
Jan van der Kruk ◽  
Giovanni Angelo Meles ◽  
Joseph Doetsch ◽  
Hansruedi Maurer ◽  
...  

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