Joint full-waveform GPR and ER inversion applied to field data acquired on the surface

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 ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. H41-H56 ◽  
Author(s):  
Xuan Feng ◽  
Qianci Ren ◽  
Cai Liu ◽  
Xuebing Zhang

Integrating crosshole ground-penetrating radar (GPR) with seismic methods is an efficient way to reduce the uncertainty and ambiguity of data interpretation in shallow geophysical investigations. We have developed a new approach for joint full-waveform inversion (FWI) of crosshole seismic and GPR data in the frequency domain to improve the inversion results of both FWI methods. In a joint objective function, three geophysical parameters (P-wave velocity, permittivity, and conductivity) are effectively connected by three weighted cross-gradient terms that enforce the structural similarity between parameter models. Simulation of acoustic seismic and scalar electromagnetic problems is implemented using 2D finite-difference frequency-domain methods, and the inverse problems of seismic FWI and GPR FWI are solved using a matrix-free truncated Newton algorithm. The joint inversion procedure is performed in several hierarchical frequencies, and the three parameter models are sequentially inverted at each frequency. The joint FWI approach is illustrated using three numerical examples. The results indicate that the joint FWI approach can effectively enhance the structural similarity among the models, modify the structure of each model, and improve the accuracy of inversion results compared with those of individual FWI approaches. Moreover, joint inversion can reduce the trade-off between permittivity and conductivity in GPR FWI, leading to an improved conductivity model in which artifacts are significantly decreased.


2015 ◽  
Vol 26 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Jan van der Kruk ◽  
Nils Gueting ◽  
Anja Klotzsche ◽  
Guowei He ◽  
Sebastian Rudolph ◽  
...  

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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