Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models

Hamann, Göran; Tronicke, Jens

doi:10.3997/1873-0604.2014005

Cited by 15 publications

(5 citation statements)

References 58 publications

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“…RMSE in terms of the space that each parameter pair encompasses. Relative RMSE values above 16 % (the maximum is 189 %) 0 20 40 are plotted in yellow and not differentiated in the colorbar, because such antenna models generated signals that show noticeable differences to the measured signal in terms of amplitude and zero-crossings. Particles with hues of blue exhibit the best fits with little to no visual differences between each other and the reference trace.…”

Section: A Optimization Results and Analysismentioning

confidence: 99%

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

Stadler

Igel

2022

IEEE Trans. Antennas Propagat.

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The antenna is the most important part of a groundpenetrating radar (GPR) system and defines the employed electromagnetic pulse and how it is transferred to the ground. It is crucial to account for these coupling effects in numerical simulations and to implement realistic antenna models, e.g., for fullwaveform inversion (FWI). We present a method of developing and adapting 3D Finite-Difference Time-Domain (FDTD) models of GPR antennas, complete with electric components, dielectric material properties and feed pulse details. We exemplify this with a commercially available, shielded 400 MHz GPR antenna, a model of which was set up by fitting synthetic data to an experimental signal of the antenna reflected at a metal plate in air. For this FWI we used a particle-swarm optimization (PSO) algorithm because the fitted parameters show complex individual effects on the GPR waveform. The resulting antenna model is then validated against data measured in air, water, and with a metal plate in the near-field of the antenna. Overall, the synthetic data reproduces the validation data very accurately. Signals of objects placed in the near-field of the antenna and the change of the shape and frequency content of the radiated wavelet with varying subsurface properties are emulated correctly.

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Section: A Optimization Results and Analysismentioning

confidence: 99%

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

Stadler

Igel

2022

IEEE Trans. Antennas Propagat.

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“…In our implementation, we set

ω = 0.7298

and

c_{1} = c_{2} = 1.4962

, which has proven to provide excellent convergence behaviour in different optimization problems (e.g., Eberhart and Shi ) including the inversion of geophysical travel time data (Tronicke et al . , ; Hamann and Tronicke ; Rumpf and Tronicke ).…”

Section: Methodsmentioning

confidence: 98%

“…Various studies have investigated the influence of these parameters on the convergence properties (e.g., Van den Bergh 2002;Elbeltagi, Hegazy, and Grierson 2005;Bratton and Kennedy 2007) and have demonstrated that results are rather insensitive to the swarm size. Following Hamann and Tronicke (2014), we use a swarm size 1.5 times the number of parameters, which shows reasonable convergence behaviour. On the other hand, the choice of ω, c 1 , and c 2 might critically influence the results and the performance of the PSO algorithm.…”

Section: E T H O D O L O G Ymentioning

confidence: 99%

“…To ensure convergence, Van den Bergh (2002) suggests to select these values in such a way that they satisfy (c 1 + c 2 /2 − 1) < ω. In our implementation, we set ω = 0.7298 and c 1 = c 2 = 1.4962, which has proven to provide excellent convergence behaviour in different optimization problems (e.g., Eberhart and Shi 2000) including the inversion of geophysical travel time data (Tronicke et al 2011Hamann and Tronicke 2014;Rumpf and Tronicke 2014).…”

Section: E T H O D O L O G Ymentioning

confidence: 99%

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Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy

Rumpf

Tronicke

2015

Geophysical Prospecting

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A B S T R A C TTo analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first-break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non-uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near-surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near-surface velocity models from refraction seismic data sets, especially in cases where no detailed a priori information regarding subsurface structures and velocity variations is available.

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“…The rms velocity approximation accounts for variable velocities under the assumption of an offset to depth of investigation ratio (O/DOI) of smaller than 0.5 (Al‐Chalabi ; Castle ) and a horizontally layered medium (Schneider ; Wiggins ). As typical GPR field data fullfil these assumptions and show only minor errors in O/DOI ratio (Hamann and Tronicke ), we simply replace the constant velocity assumption in equation by the space and time dependent rms velocity approximation (equation ) resulting in

\begin{array}{r} right & t^{'} {(x, y, z)}^{2} = t_{0}^{2} + 4 \frac{{(x - x_{0})}^{2} + {(y - y_{0})}^{2} + {(z - z_{0})}^{2}}{v_{r m s}^{2}} \\ right & + 4 \frac{t_{0} (z - z_{0})}{v_{r m s}} . \end{array}

…”

Section: Methodsmentioning

confidence: 99%

Topographic migration of 2D and 3D ground‐penetrating radar data considering variable velocities

Allroggen

Tronicke

Delock

et al. 2014

Near Surface Geophysics

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We present a 2D/3D topographic migration scheme for ground‐penetrating radar (GPR) data which is able to account for variable velocities by using the root mean square (rms) velocity approximation. We test our migration scheme using a synthetic 2D example and compare our migrated image to the results obtained using common GPR migration approaches. Furthermore, we apply it to 2D and 3D field data. These examples are recorded across common subsurface settings including surface topography and variations in the GPR subsurface velocity field caused by a shallow ground water table. In such field settings, our migration strategy provides well focused images of common‐offset GPR data without the need for a detailed interval velocity model. The synthetic and field examples demonstrate that our topographic migration scheme allows for accurate GPR imaging in the presence of variations in surface topography and subsurface velocity.

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Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models

Cited by 15 publications

References 58 publications

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

Developing Realistic FDTD GPR Antenna Surrogates by Means of Particle Swarm Optimization

Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy

Topographic migration of 2D and 3D ground‐penetrating radar data considering variable velocities

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