A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters From Combination Crosshole/Borehole-to-Surface GPR Data

Meles, Giovanni Angelo; Kruk, Jan van der; Greenhalgh, Stewart; Ernst, Jacques; Maurer, Hansruedi; Green, Alan G.

doi:10.1109/tgrs.2010.2046670

Cited by 186 publications

(135 citation statements)

References 45 publications

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…In crosshole GPR full-waveform inversion the long spatial wavelengths of the model are typically used as a starting model. This model is obtained through ray-based inversion of first-arrival traveltimes and amplitudes of the waveform data (Ernst et al, 2007a;Meles et al, 2010). The data set considered in our study is expected to be too sparse to provide any useful information for a ray-based starting model.…”

Section: Discussionmentioning

confidence: 99%

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

2012

View full text Add to dashboard Cite

We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework for a full-waveform inversion strategy that integrates the extended Metropolis algorithm with sequential Gibbs sampling such that arbitrary complex geostatistically defined a priori information can be included. At the same time we show how temporally and/or spatially correlated data uncertainties can be taken into account during the inversion. The suggested inversion strategy is tested on synthetic tomographic crosshole ground-penetrating radar full-waveform data using multiple-point-based a priori information. This is, to our knowledge, the first example of obtaining a posteriori realizations of a full-waveform inverse problem. Benefits of the proposed methodology compared with deterministic inversion approaches include: (1) The a posteriori model variability reflects the states of information provided by the data uncertainties and a priori information, which provides a means of obtaining resolution analysis. (2) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional methods do. Moreover, an adequate knowledge of data uncertainties and a priori information is required to obtain meaningful uncertainty estimates. The latter may be a key challenge when considering field experiments, which will not be addressed here.

show abstract

Section: Discussionmentioning

confidence: 99%

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

2012

View full text Add to dashboard Cite

show abstract

“…The full-waveform inversion (FWI) approach used for this work is the FWI algorithm of Meles et al (2010), which extended the work of Ernst et al (2007) by honoring the EM vector character and introducing a simultaneous updating of the electrical permittivity and conductivity.…”

Section: Gpr Post-processing and Inversionmentioning

confidence: 99%

Mapping sand layers in clayey till using crosshole ground-penetrating radar

et al. 2018

Self Cite

View full text Add to dashboard Cite

Fluid transport through clayey tills governs the quantity and quality of groundwater resources in the Northern hemisphere. This transport is often controlled by a three-dimensional network of macropores (biopores, fractures and sand lenses) within the clayey till. At present, a non-destructive technique that can map and characterize the sand-lens-network does not exist, and full excavation or extensive drilling is therefore the only solution. Acquisition and modeling of crosshole ground penetrating radar (GPR) may provide the answer to this problem. We collected one-and two-dimensional crosshole GPR data at a field site in Denmark between four 8-m-deep boreholes with horizontal distances varying between 2.64-5.05 m. We show that the depth, thickness, and tilt of a coherent sand layer within the clayey till (approximately 0.4-0.6 m thick) as well as the underlying sand formation can be mapped accurately using the GPR data.We identified the sand efficiently as a highly resistive section with high electromagnetic wave velocities, while the clayey till was conductive with lower electromagnetic wave velocities. We found that the exact location of the sand occurrences was better delineated by the increase in amplitude than the increase in electromagnetic wave velocity. We believe that crosshole GPR may contribute significantly to groundwater protection and contaminant remediation initiatives.

show abstract

“…where ( ) , s syn E ε σ and s obs E are the synthetic and observed data, respectively, that contain the data for all sources, receivers and observation times according to the Meles et al (2010) formalism. Here, T denotes the transpose conjugate and the synthetic data are calculated using a 2D finite-difference time-domain (FDTD) solution of Maxwell's equations.…”

Section: Full-waveform Inversion Of Crosshole Gpr Datamentioning

confidence: 99%

“…Here, T denotes the transpose conjugate and the synthetic data are calculated using a 2D finite-difference time-domain (FDTD) solution of Maxwell's equations. The gradients of the misfit function with respect to the permittivity and conductivity S ∇ ε , and S ∇ σ are obtained by cross-correlating the incident wave field emitted from the source with the residual wave fields ∆E S that are back-propagating using the backpropagation operator ˆT G from the receiver at all medium locations in the time domain for all sources and receiver combinations as follows (see also Yang et al, 2013b;Meles et al, 2010) …”

Section: Full-waveform Inversion Of Crosshole Gpr Datamentioning

confidence: 99%

Quantitative multi-layer electromagnetic induction inversion and full-waveform inversion of crosshole ground penetrating radar data

et al. 2015

Self Cite

View full text Add to dashboard Cite

Due to the recent system developments for the electromagnetic characterization of the subsurface, fast and easy acquisition is made feasible due to the fast measurement speed, easy coupling with GPS systems, and the availability of multi-channel electromagnetic induction (EMI) and ground penetrating radar (GPR) systems. Moreover, the increasing computer power enables the use of accurate forward modeling programs in advanced inversion algorithms where no approximations are used and the full information content of the measured data can be exploited. Here, recent developments of large-scale quantitative EMI inversion and full-waveform GPR inversion are discussed that yield higher resolution of quantitative medium properties compared to conventional approaches. In both cases a detailed forward model is used in the inversion procedure that is based on Maxwell's equations. The multi-channel EMI data that have different sensing depths for the different source-receiver offset are calibrated using a short electrical resistivity tomography (ERT) calibration line which makes it possible to invert for electrical conductivity changes with depth over large areas. The crosshole GPR full-waveform inversion yields significant higher resolution of the permittivity and conductivity images compared to ray-based inversion results. KEY WORDS: ground penetrating radar, electromagnetic induction, full-waveform inversion. INTRODUCTIONThe electromagnetic tools, EMI and GPR, can be used for a wide range of applications to non-invasively image the subsurface. Due to the fast data acquisition, where the measured data can be directly linked with high resolution GPS systems, it is becoming more and more feasible to map GPR and EMI over large areas. The use of multi-channel EMI and GPR makes it possible to acquire simultaneously EMI and GPR data for different source-receiver offsets that enable an improved subsurface characterization. Ray-based or approximate techniques are often used where only part of the data is exploited. Improved subsurface characterization can be obtained by including advanced modeling tools that are able to calculate the electromagnetic wave propagation with high accuracy using Maxwell's equations. In the following, we will describe recent advancements in the high-resolution imaging of EMI and GPR data.

show abstract

A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters From Combination Crosshole/Borehole-to-Surface GPR Data

Cited by 186 publications

References 45 publications

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

Mapping sand layers in clayey till using crosshole ground-penetrating radar

Quantitative multi-layer electromagnetic induction inversion and full-waveform inversion of crosshole ground penetrating radar data

Contact Info

Product

Resources

About