Introduction to this special section: Near-surface imaging and modeling

This special section on near-surface imaging and modeling was intended originally to focus on improving deeper imaging for exploration purposes through more accurate representations of the near surface, the highly variable zone that energy must traverse through on the way down and back up again to be recorded at the surface. However, as proposed manuscript topics started coming in, it became clear that this section would cover a wider range, from kilometers down to meters. Papers in this section highlight a range of near-surface-related work that includes applying full-waveform inversion (FWI) to improve near-surface velocity models, identifying potential sinkhole hazards before they collapse, the potential of smartphones as geophysical sensors, and new open-source software for ground-penetrating radar data.

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Full Waveform Inversion in generalized coordinates for zones of curved topography

CT&F - Ciencia Tecnología y Futuro ◽

10.29047/01225383.84 ◽

2018 ◽

Vol 8 (2) ◽

Author(s):

César Augusto Arias- Chica ◽

David Abreo ◽

Sergio Abreo ◽

Luis Fernando Duque- Gómez ◽

Ana Beatríz Ramírez- Silva

Keyword(s):

Computing Time ◽

Waveform Inversion ◽

Surface Velocity ◽

Full Waveform Inversion ◽

Near Surface ◽

Velocity Models ◽

Full Waveform ◽

Wide Range ◽

Rugged Topography ◽

Uniform Rectangular Grid

Full waveform inversion (FWI) has been recently used to estimate subsurface parameters, such as velocity models. This method, however, has a number of drawbacks when applied to zones with rugged topography due to the forced application of a Cartesian mesh on a curved surface. In this work, we present a simple coordinate transformation that enables the construction of a curved mesh. The proposed transformation is more suitable for rugged surfaces and it allows mapping a physical curved domain into a uniform rectangular grid, where acoustic FWI can be applied in the traditional way by introducing a modified Laplacian. We prove that the proposed approximation can have a wide range of applications, producing precise near-surface velocity models without increasing the computing time of the FWI.

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

Journal of Earth Science ◽

10.1007/s12583-015-0610-3 ◽

2015 ◽

Vol 26 (6) ◽

pp. 844-850 ◽

Cited By ~ 9

Author(s):

Jan van der Kruk ◽

Nils Gueting ◽

Anja Klotzsche ◽

Guowei He ◽

Sebastian Rudolph ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Electromagnetic Induction ◽

Waveform Inversion ◽

Radar Data ◽

Full Waveform Inversion ◽

Full Waveform ◽

Ground Penetrating

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Elastic Full Waveform Inversion in a Space Frequency Formulation for Near-Surface Imaging

10.3997/2214-4609-pdb.1.p274 ◽

2005 ◽

Author(s):

C. Gelis ◽

J. Vireux ◽

G. Grandjean ◽

S. Operto

Keyword(s):

Waveform Inversion ◽

Full Waveform Inversion ◽

Surface Imaging ◽

Near Surface ◽

Full Waveform ◽

Space Frequency

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Application of Laplace Domain Waveform Inversion to Cross-Hole Radar Data

Remote Sensing ◽

10.3390/rs11161839 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1839

Author(s):

Xu Meng ◽

Sixin Liu ◽

Yi Xu ◽

Lei Fu

Keyword(s):

Time Domain ◽

Waveform Inversion ◽

Radar Data ◽

Dominant Frequency ◽

Data Set ◽

Laplace Domain ◽

Full Waveform ◽

Highly Nonlinear ◽

The Time Domain ◽

Ground Penetrating

Full waveform inversion (FWI) can yield high resolution images and has been applied in Ground Penetrating Radar (GPR) for around 20 years. However, appropriate selection of the initial models is important in FWI because such an inversion is highly nonlinear. The conventional way to obtain the initial models for GPR FWI is ray-based tomogram inversion which suffers from several inherent shortcomings. In this paper, we develop a Laplace domain waveform inversion to obtain initial models for the time domain FWI. The gradient expression of the Laplace domain waveform inversion is deduced via the derivation of a logarithmic object function. Permittivity and conductivity are updated by using the conjugate gradient method. Using synthetic examples, we found that the value of the damping constant in the inversion cannot be too large or too small compared to the dominant frequency of the radar data. The synthetic examples demonstrate that the Laplace domain waveform inversion provide slightly better initial models for the time domain FWI than the ray-based inversion. Finally, we successfully applied the algorithm to one field data set, and the inverted results of the Laplace-based FWI show more details than that of the ray-based FWI.

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Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

The APPEA Journal ◽

10.1071/aj17229 ◽

2018 ◽

Vol 58 (2) ◽

pp. 884

Author(s):

Lianping Zhang ◽

Haryo Trihutomo ◽

Yuelian Gong ◽

Bee Jik Lim ◽

Alexander Karvelas

Keyword(s):

Model Building ◽

Waveform Inversion ◽

Velocity Model ◽

Surface Velocity ◽

Image Point ◽

Surface Model ◽

Full Waveform Inversion ◽

Near Surface ◽

North West ◽

Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

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Multiparametric full waveform inversion of 2D ground penetrating radar data using multiple GPUs

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV ◽

10.1117/12.2516053 ◽

2019 ◽

Author(s):

Jheyston Serrano ◽

Ana Ramirez ◽

Sergio Alberto Abreo ◽

Brian Sadler

Keyword(s):

Ground Penetrating Radar ◽

Waveform Inversion ◽

Radar Data ◽

Full Waveform Inversion ◽

Multiple Gpus ◽

Full Waveform ◽

Ground Penetrating

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Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽

10.1190/geo2020-0474.1 ◽

2020 ◽

pp. 1-57

Author(s):

Daniele Colombo ◽

Ernesto Sandoval ◽

Diego Rovetta ◽

Apostolos Kontakis

Keyword(s):

High Resolution ◽

Seismic Data ◽

Waveform Inversion ◽

Surface Velocity ◽

Full Waveform Inversion ◽

Velocity Mapping ◽

Signal To Noise ◽

Near Surface ◽

Velocity Modeling ◽

Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

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