Taming the non-linearity problem in GPR full-waveform inversion for high contrast media

Full-waveform inversion (FWI) can provide accurate estimates of subsurface model parameters. In spite of its success, the application of FWI in areas with high-velocity contrast remains a challenging problem. Quadratic regularization methods are often adopted to stabilize inverse problems. Unfortunately, edges and sharp discontinuities are not adequately preserved by quadratic regularization techniques. Throughout the iterative FWI method, an edge-preserving filter, however, can gently incorporate sharpness into velocity models. For every point in the velocity model, edge-preserving smoothing assigns the average value of the most uniform window neighboring the point. Edge-preserving smoothing generates piecewise-homogeneous images with enhanced contrast at boundaries. We adopt a simultaneous-source frequency-domain FWI, based on quasi-Newton optimization, in conjunction with an edge-preserving smoothing filter to retrieve high-contrast velocity models. The edge-preserving smoothing filter gradually removes the artifacts created by simultaneous-source encoding. We also have developed a simple model update to prevent disrupting the convergence of the optimization algorithm. Finally, we perform tests to examine our algorithm.

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Erratum to: Implementing bound constraints and total-variation regularization in extended full-waveform inversion with the alternating direction method of multiplier: application to large contrast media

Geophysical Journal International ◽

10.1093/gji/ggz386 ◽

2019 ◽

Vol 219 (2) ◽

pp. 1431-1431

Author(s):

H S Aghamiry ◽

A Gholami ◽

S Operto

Keyword(s):

Contrast Media ◽

Total Variation ◽

Waveform Inversion ◽

Alternating Direction Method ◽

Total Variation Regularization ◽

Full Waveform Inversion ◽

Bound Constraints ◽

Full Waveform ◽

Alternating Direction

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2.5D crosshole GPR full-waveform inversion with synthetic and measured data

Geophysics ◽

10.1190/geo2019-0600.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. H71-H82

Author(s):

Amirpasha Mozaffari ◽

Anja Klotzsche ◽

Craig Warren ◽

Guowei He ◽

Antonios Giannopoulos ◽

...

Keyword(s):

Electrical Conductivity ◽

Waveform Inversion ◽

Computational Effort ◽

Measured Data ◽

Superior Performance ◽

Full Waveform Inversion ◽

High Contrast ◽

Model Errors ◽

Full Waveform ◽

2D Data

Full-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.

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