Joint inversion of GPR and ER data

Domenzain, Diego; Bradford, John H.; Mead, Jodi

doi:10.1190/segam2018-2997794.1

Cited by 5 publications

(1 citation statement)

References 80 publications

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“…We imagine a future in which additional observations such as magnetic anomaly measurements (Lewis and Simons 2012), electric resistivity probings (Robbins and Plattner 2018) and ground-penetrating radar (Domenzain et al 2018;Plattner 2020), would form part and parcel of a holistic geophysical inversion scheme. Even within our own limited framework we imagine including additional complexity in the physical description of the Earth system, such as incorporating full elasticity and even anelastic viscous dampening of the seismic wavefield (Askan et al 2007(Askan et al , 2010Pan and Wang 2020;Tarantola 1986Tarantola , 1988, including visco-elasto-plastic effects in the Stokes equation (Peltier 1985), and allowing for additional kernel evaluations, e.g., for internal friction, with additional coupling between Hooke's and Stoke's problems through the elastic shear modulus.…”

Section: Discussionmentioning

confidence: 99%

Multi-physics adjoint modeling of Earth structure: combining gravimetric, seismic, and geodynamic inversions

Reuber

Simons

2020

Int J Geomath

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We discuss the resolving power of three geophysical imaging and inversion techniques, and their combination, for the reconstruction of material parameters in the Earth’s subsurface. The governing equations are those of Newton and Poisson for gravitational problems, the acoustic wave equation under Hookean elasticity for seismology, and the geodynamics equations of Stokes for incompressible steady-state flow in the mantle. The observables are the gravitational potential, the seismic displacement, and the surface velocity, all measured at the surface. The inversion parameters of interest are the mass density, the acoustic wave speed, and the viscosity. These systems of partial differential equations and their adjoints were implemented in a single Python code using the finite-element library FeNICS. To investigate the shape of the cost functions, we present a grid search in the parameter space for three end-member geological settings: a falling block, a subduction zone, and a mantle plume. The performance of a gradient-based inversion for each single observable separately, and in combination, is presented. We furthermore investigate the performance of a shape-optimizing inverse method, when the material is known, and an inversion that inverts for the material parameters of an anomaly with known shape.

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Section: Discussionmentioning

confidence: 99%

Multi-physics adjoint modeling of Earth structure: combining gravimetric, seismic, and geodynamic inversions

Reuber

Simons

2020

Int J Geomath

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Review of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls

2019

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Heterogeneous small-scale high-contrast layers and spatial variabilities of soil properties can have a large impact on flow and transport processes in the critical zone. Because their characterization is difficult and critical, high-resolution methods are required. Standard ray-based approaches for imaging the subsurface consider only a small amount of the measured data and suffer from limited resolution. In contrast, full-waveform inversion (FWI) considers the full information content of the measured data and could yield higher resolution images in the subwavelength scale. In the past few decades, ground-penetrating radar (GPR) FWI and its application to experimental data have matured, which makes GPR FWI an established approach to significantly improve resolution. Several theoretical developments were achieved to improve the application to experimental data from crosshole GPR FWI. We have determined the necessary steps to perform FWI for experimental data to obtain reliable and reproducible high-resolution images. We concentrate on experimental crosshole GPR data from a test site in Switzerland to illustrate the challenges of applying FWI to experimental data and discuss the obtained results for different development steps including possible pitfalls. Thereby, we acknowledge out the importance of a correct time-zero correction of the data, the estimation of the effective source wavelet, and the effect of the choice of starting models. The reliability of the FWI results is investigated by analyzing the fit of the measured and modeled traces, the remaining gradients of the final models, and validating with independently measured logging data. Thereby, we found that special care needs to be taken to define the optimal inversion parameters to avoid overshooting of the inversion or truncation errors.

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Joint inversion of full-waveform ground-penetrating radar and electrical resistivity data: Part 1

2020

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We develop an algorithm for joint inversion of full-waveform ground-penetrating radar (GPR) and electrical resistivity (ER) data. GPR is sensitive to electrical permittivity through reflectivity and velocity, and electrical conductivity through reflectivity and attenuation. ER is directly sensitive to electrical conductivity. The two types of data are inherently linked through Maxwell's equations and we jointly invert them. 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 both 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 assumption of the subsurface geometry nor structural similarities between parameters with the caveat of needing a good initial model. We find that our joint inversion outperforms both GPR and ER separate inversions and determine that GPR effectively supports ER in regions of low conductivity while ER supports GPR in regions with strong attenuation.

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Joint inversion of GPR and ER data

Cited by 5 publications

References 80 publications

Multi-physics adjoint modeling of Earth structure: combining gravimetric, seismic, and geodynamic inversions

Multi-physics adjoint modeling of Earth structure: combining gravimetric, seismic, and geodynamic inversions

Review of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls

Joint inversion of full-waveform ground-penetrating radar and electrical resistivity data: Part 1

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