2.5D crosshole GPR full-waveform inversion with synthetic and measured 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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Explicit High-contrast Surface Refinement Using Full Waveform Inversion

Proceedings 76th EAGE Conference and Exhibition 2014 ◽

10.3997/2214-4609.20140705 ◽

2014 ◽

Author(s):

J.W.D. Hobro ◽

J.E. Rickett

Keyword(s):

Waveform Inversion ◽

Full Waveform Inversion ◽

High Contrast ◽

Full Waveform ◽

Surface Refinement

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Quantitative conductivity and permittivity estimation using full-waveform inversion of on-ground GPR data

Geophysics ◽

10.1190/geo2012-0045.1 ◽

2012 ◽

Vol 77 (6) ◽

pp. H79-H91 ◽

Cited By ~ 58

Author(s):

Sebastian Busch ◽

Jan van der Kruk ◽

Jutta Bikowski ◽

Harry Vereecken

Keyword(s):

Quantitative Estimation ◽

Waveform Inversion ◽

Measured Data ◽

Optimization Approach ◽

Full Waveform Inversion ◽

Near Surface ◽

Full Waveform ◽

Novel Approach ◽

Wide Range ◽

Starting Model

Conventional ray-based techniques for analyzing common-midpoint (CMP) ground-penetrating radar (GPR) data use part of the measured data and simplified approximations of the reality to return qualitative results with limited spatial resolution. Whereas these methods can give reliable values for the permittivity of the subsurface by employing only the phase information, the far-field approximations used to estimate the conductivity of the ground are not valid for near-surface on-ground GPR, such that the estimated conductivity values are not representative for the area of investigation. Full-waveform inversion overcomes these limitations by using an accurate forward modeling and inverts significant parts of the measured data to return reliable quantitative estimates of permittivity and conductivity. Here, we developed a full-waveform inversion scheme that uses a 3D frequency-domain solution of Maxwell’s equations for a horizontally layered subsurface. Although a straightforward full-waveform inversion is relatively independent of the permittivity starting model, inaccuracies in the conductivity starting model result in erroneous effective wavelet amplitudes and therefore in erroneous inversion results, because the conductivity and wavelet amplitudes are coupled. Therefore, the permittivity and conductivity are updated together with the phase and the amplitude of the source wavelet with a gradient-free optimization approach. This novel full-waveform inversion is applied to synthetic and measured CMP data. In the case of synthetic single layered and waveguide data, where the starting model differs significantly from the true model parameter, we were able to reconstruct the obtained model properties and the effective source wavelet. For measured waveguide data, different starting values returned the same wavelet and quantitative permittivities and conductivities. This novel approach enables the quantitative estimation of permittivity and conductivity for the same sensing volume and enables an improved characterization for a wide range of applications.

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High-resolution depth imaging of long-offset 2D data using full-waveform inversion: The Malvinas Basin, Argentina

The Leading Edge ◽

10.1190/tle38030220.1 ◽

2019 ◽

Vol 38 (3) ◽

pp. 220-225

Author(s):

Laurence Letki ◽

Mike Saunders ◽

Monica Hoppe ◽

Milos Cvetkovic ◽

Lewis Goss ◽

...

Keyword(s):

High Resolution ◽

Waveform Inversion ◽

Velocity Model ◽

Full Waveform Inversion ◽

Data Set ◽

Depth Imaging ◽

Additional Information ◽

Full Waveform ◽

Seismic Image ◽

2D Data

The Argentina Austral Malvinas survey comprises 13,784 km of 2D data extending from the shelf to the border with the Falkland Islands. The survey was acquired using a 12,000 m streamer and continuous recording technology and was processed through a comprehensive broadband prestack depth migration workflow focused on producing a high-resolution, high-fidelity data set. Source- and receiver-side deghosting to maximize the bandwidth of the data was an essential ingredient in the preprocessing. Following the broadband processing sequence, a depth-imaging workflow was implemented, with the initial model built using a time tomography approach. Several passes of anisotropic reflection tomography provided a significant improvement in the velocity model prior to full-waveform inversion (FWI). Using long offsets, FWI made use of additional information contained in the recorded wavefield, including the refracted and diving wave energy. FWI resolved more detailed velocity variations both in the shallow and deeper section and culminated in an improved seismic image.

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Edge-preserving smoothing for simultaneous-source full-waveform inversion model updates in high-contrast velocity models

Geophysics ◽

10.1190/geo2017-0563.1 ◽

2018 ◽

Vol 83 (2) ◽

pp. A33-A37 ◽

Cited By ~ 5

Author(s):

Amsalu Y. Anagaw ◽

Mauricio D. Sacchi

Keyword(s):

Waveform Inversion ◽

Velocity Model ◽

Model Parameters ◽

Full Waveform Inversion ◽

High Contrast ◽

Edge Preserving ◽

Velocity Models ◽

Smoothing Filter ◽

Full Waveform ◽

Simultaneous Source

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

Journal of Applied Geophysics ◽

10.1016/j.jappgeo.2011.01.001 ◽

2011 ◽

Vol 73 (2) ◽

pp. 174-186 ◽

Cited By ~ 31

Author(s):

Giovanni Meles ◽

Stewart Greenhalgh ◽

Jan van der Kruk ◽

Alan Green ◽

Hansruedi Maurer

Keyword(s):

Contrast Media ◽

Waveform Inversion ◽

Full Waveform Inversion ◽

High Contrast ◽

Full Waveform

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Parametric convolutional neural network-domain full-waveform inversion

Geophysics ◽

10.1190/geo2018-0224.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. R881-R896 ◽

Cited By ~ 6

Author(s):

Yulang Wu ◽

George A. McMechan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Initial Velocity ◽

Prior Information ◽

Waveform Inversion ◽

Velocity Model ◽

Full Waveform Inversion ◽

Model Errors ◽

Initial Model ◽

Full Waveform

Most current full-waveform inversion (FWI) algorithms minimize the data residuals to estimate a velocity model based on the assumption that the updated model is the sum of a background model and an estimated model perturbation. We have performed reparameterization of the initial velocity model, by the weights in a convolutional neural network (CNN), to automatically capture the salient features in the initial model, as a priori information. The prior information in CNN weights is iteratively updated as regularization to constrain the CNN-domain inversion to refine the features captured in CNN pretraining by reducing the data misfit. Synthetic examples using a 1D increasing velocity function v(z) and a 2D smoothed version of the correct Marmousi2 model as initial models indicate that the performance of the CNN-domain FWI depends on the existence and accuracy of the prior information in the initial velocity model (i.e., whether features whose positions, shapes, and values are present in the correct model are approximately included in the initial model). Forty different sets of randomly initialized CNN weights are used to parameterize and test CNN-domain FWI, using a 2D smoothed Sigsbee model as the initial velocity model. All 40 sets invert for the Sigsbee salt body more accurately (with a smaller standard deviation of the final rms model errors), by CNN-domain FWI, than FWI does. Features that are not represented within the CNN hidden layers in the initial velocity model, and so cannot be recovered by CNN-domain FWI, can be recovered using the final CNN-domain FWI velocity model as the starting model in a subsequent conventional FWI.

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Full waveform inversion updating in the presence of high angle/high contrast reflectivity

10.1190/segam2015-5924279.1 ◽

2015 ◽

Author(s):

K. A. Innanen

Keyword(s):

Waveform Inversion ◽

Full Waveform Inversion ◽

High Contrast ◽

High Angle ◽

Full Waveform

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Robust full-waveform inversion with Radon-domain matching filter

Geophysics ◽

10.1190/geo2018-0347.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. R707-R724 ◽

Cited By ~ 5

Author(s):

Bingbing Sun ◽

Tariq Alkhalifah

Keyword(s):

Radon Transform ◽

Waveform Inversion ◽

Delta Function ◽

Measured Data ◽

Dirac Delta Function ◽

Full Waveform Inversion ◽

Dirac Delta ◽

Full Waveform ◽

Matching Filter ◽

Filter Approach

Cycle skipping is a severe issue in full-waveform inversion. One option to overcome it is to extend the search space to allow for data comparisons beyond the “point-to-point” subtraction. A matching filter can be computed by deconvolving the measured data from the predicted ones. If the model is correct, the resulting matching filter would be a Dirac delta function in which the energy is focused at zero lag. An optimization problem can be formulated by penalizing this matching filter departure from a Dirac delta function. Because the matching filter replaces the local sample-by-sample comparison with a global one using deconvolution, it can reduce the cycle-skipping problem. Because the matching filter is computed using the whole trace of the measured and predicted data, it is prone to unwanted crosstalk of different events. We perform the deconvolution in the Radon domain to reduce crosstalk and improve the inversion. We first transform the measured and the predicted data into the [Formula: see text] domain using the local Radon transform. We then perform deconvolution for the trace indexed by the same slope value. The main objective of the proposal is to use the slope information embedded in the Radon-transform representation to separate the events and reduce the crosstalk in the deconvolution step. As a result, the objective function tends to be more convex and stabilizes the inversion process. The result obtained for the modified Marmousi model demonstrates the proposed Radon-domain matching-filter approach can converge to a meaningful model given data without the low frequencies of less than 3 Hz and a [Formula: see text] initial model. Compared to the conventional time-space matching-filter approach, the Radon-domain approach indicates fewer artifacts in the model and better fitting of the measured data. The result corresponding to the Chevron 2014 benchmark data set also indicates the good performance of the proposed approach.

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

Geophysics ◽

10.1190/geo2018-0597.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. H13-H28 ◽

Cited By ~ 7

Author(s):

Anja Klotzsche ◽

Harry Vereecken ◽

Jan van der Kruk

Keyword(s):

Experimental Data ◽

High Resolution ◽

Ground Penetrating Radar ◽

Waveform Inversion ◽

Measured Data ◽

Transport Processes ◽

Small Scale ◽

Full Waveform Inversion ◽

Full Waveform ◽

Ground Penetrating

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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