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Journal articles on the topic 'Full wavefrom inversion'

Author: Grafiati

Published: 5 October 2024

Last updated: 31 July 2025

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1

Liu, Yike, Bin He, Huiyi Lu, Zhendong Zhang, Xiao-Bi Xie, and Yingcai Zheng. "Full-intensity waveform inversion." GEOPHYSICS 83, no. 6 (2018): R649—R658. http://dx.doi.org/10.1190/geo2017-0682.1.

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Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operati

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2

Ha, Wansoo, and Changsoo Shin. "Laplace-domain full-waveform inversion of seismic data lacking low-frequency information." GEOPHYSICS 77, no. 5 (2012): R199—R206. http://dx.doi.org/10.1190/geo2011-0411.1.

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The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the

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Zhang, Tuo, and Christoph Sens-Schönfelder. "Adjoint envelope tomography for scattering and absorption using radiative transfer theory." Geophysical Journal International 229, no. 1 (2021): 566–88. http://dx.doi.org/10.1093/gji/ggab457.

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SUMMARY To investigate the small-scale elastic structure of the subsurface at length scales below the resolution limits of waveform tomography, envelopes of high-frequency scattered seismic waveforms have been used with a variety of approaches. However, a rigorous framework for the iterative inversion of seismogram envelopes to image heterogeneity and high-frequency attenuation comparable to full waveform inversion (FWI) is missing. We present the mathematical framework for an iterative full envelope inversion using forward and adjoint simulations of the radiative transfer equations, in full a

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Smithyman, Brendan R., and Ronald M. Clowes. "Waveform tomography of field vibroseis data using an approximate 2D geometry leads to improved velocity models." GEOPHYSICS 77, no. 1 (2012): R33—R43. http://dx.doi.org/10.1190/geo2011-0076.1.

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Waveform tomography, a combination of traveltime tomography (or inversion) and waveform inversion, is applied to vibroseis first-arrival data to generate an interpretable model of P-wave velocity for a site in the Nechako Basin, south-central British Columbia, Canada. We use constrained 3D traveltime inversion followed by 2D full-waveform inversion to process long-offset (14.4 km) first-arrival refraction waveforms, resulting in a velocity model of significantly higher detail than a conventional refraction-statics model generated for a processing workflow. The crooked-line acquisition of the d

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AlTheyab, Abdullah, and G. T. Schuster. "Wavefront picking for 3D tomography and full-waveform inversion." GEOPHYSICS 81, no. 6 (2016): B201—B210. http://dx.doi.org/10.1190/geo2015-0544.1.

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We have developed an efficient approach for picking first-break wavefronts on coarsely sampled time slices of 3D shot gathers. Our objective was to compute a smooth initial velocity model for multiscale full-waveform inversion (FWI). Using interactive software, first-break wavefronts were geometrically modeled on time slices with a minimal number of picks. We picked sparse time slices, performed traveltime tomography, and then compared the predicted traveltimes with the data in-between the picked slices. The picking interval was refined with iterations until the errors in traveltime prediction

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Xing, Zhen, and Alfredo Mazzotti. "Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 2: Application to two actual data sets." GEOPHYSICS 84, no. 5 (2019): R815—R825. http://dx.doi.org/10.1190/geo2018-0800.1.

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We have applied our two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI) to two actual data sets acquired in Luni (Italy) and Grenoble (France), respectively. Because our technique used 2D elastic finite-difference modeling for solving the forward problem, the observed data were 3D to 2D corrected prior to the inversion. To limit the computing time, both inversions focused on predicting low-resolution, smooth models by using quite coarse inversion grids. The wavelets for FWI were estimated directly from the observed data by using the Wiener method. In the Luni case, due to th

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7

Zhang, Zhen-dong, and Tariq Alkhalifah. "Local-crosscorrelation elastic full-waveform inversion." GEOPHYSICS 84, no. 6 (2019): R897—R908. http://dx.doi.org/10.1190/geo2018-0660.1.

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Full-waveform inversion (FWI) in its classic form is a method based on minimizing the [Formula: see text] norm of the difference between the observed and simulated seismic waveforms at the receiver locations. The objective is to find a subsurface model that reproduces the full waveform including the traveltimes and amplitudes of the observed seismic data. However, the widely used [Formula: see text]-norm-based FWI faces many issues in practice. The point-wise comparison of waveforms fails when the phase difference between the compared waveforms of the predicted and observed data is larger than

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8

Maurer, Hansruedi, Stewart A. Greenhalgh, Edgar Manukyan, Stefano Marelli, and Alan G. Green. "Receiver-coupling effects in seismic waveform inversions." GEOPHYSICS 77, no. 1 (2012): R57—R63. http://dx.doi.org/10.1190/geo2010-0402.1.

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Seismic waveform-inversion offers opportunities for detailed characterization of the subsurface. However, its full potential can only be exploited when any systematic source and receiver effects are either carefully avoided or appropriately accounted for during the inversions. Repeated crosshole measurements in the Mont Terri (Switzerland) underground laboratory have revealed that receiver coupling may significantly affect the seismic waveforms. More seriously, coupling conditions may vary during the course of a monitoring experiment. To address this problem, we have developed a novel scheme t

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9

Lu, Cai, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, and Jiandong Liang. "Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition." Applied Sciences 15, no. 2 (2025): 941. https://doi.org/10.3390/app15020941.

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Full-waveform velocity inversion has long been a primary focus in seismic exploration. Full-waveform inversion techniques employing physics-informed recurrent neural networks (PIRNNs) have recently gained significant scholarly attention. However, these approaches demand considerable storage to capture spatiotemporal seismic wave propagation fields and their gradient information, often exceeding the memory capabilities of current GPU resources during field data processing. This study proposes a full-waveform inversion method utilizing a dual-branch PIRNN architecture to effectively minimize GPU

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10

Bleibinhaus, Florian, and Stéphane Rondenay. "Effects of surface scattering in full-waveform inversion." GEOPHYSICS 74, no. 6 (2009): WCC69—WCC77. http://dx.doi.org/10.1190/1.3223315.

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In full-waveform inversion of seismic body waves, often the free surface is ignored on grounds of computational efficiency. A synthetic study was performed to investigate the effects of this simplification. In terms of size and frequency, the test model and data conform to a real long-offset survey of the upper crust across the San Andreas fault. Random fractal variations are superimposed on a background model with strong lateral and vertical velocity variations ranging from 1200 to 6800 m/s. Synthetic data were computed and inverted for this model and different topographies. A fully viscoelas

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Charara, Marwan, and Christophe Barnes. "Constrained Full Waveform Inversion for Borehole Multicomponent Seismic Data." Geosciences 9, no. 1 (2019): 45. http://dx.doi.org/10.3390/geosciences9010045.

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Full-waveform inversion for borehole seismic data is an ill-posed problem and constraining the problem is crucial. Constraints can be imposed on the data and model space through covariance matrices. Usually, they are set to a diagonal matrix. For the data space, signal polarization information can be used to evaluate the data uncertainties. The inversion forces the synthetic data to fit the polarization of observed data. A synthetic inversion for a 2D-2C data estimating a 1D elastic model shows a clear improvement, especially at the level of the receivers. For the model space, horizontal and v

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Brossier, Romain, Stéphane Operto, and Jean Virieux. "Seismic imaging of complex onshore structures by 2D elastic frequency-domain full-waveform inversion." GEOPHYSICS 74, no. 6 (2009): WCC105—WCC118. http://dx.doi.org/10.1190/1.3215771.

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Quantitative imaging of the elastic properties of the subsurface at depth is essential for civil engineering applications and oil- and gas-reservoir characterization. A realistic synthetic example provides for an assessment of the potential and limits of 2D elastic full-waveform inversion (FWI) of wide-aperture seismic data for recovering high-resolution P- and S-wave velocity models of complex onshore structures. FWI of land data is challenging because of the increased nonlinearity introduced by free-surface effects such as the propagation of surface waves in the heterogeneous near-surface. M

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13

Fichtner, Andreas, Jeannot Trampert, Paul Cupillard, et al. "Multiscale full waveform inversion." Geophysical Journal International 194, no. 1 (2013): 534–56. http://dx.doi.org/10.1093/gji/ggt118.

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14

Zhang, Xin, and Andrew Curtis. "Variational full-waveform inversion." Geophysical Journal International 222, no. 1 (2020): 406–11. http://dx.doi.org/10.1093/gji/ggaa170.

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SUMMARY Seismic full-waveform inversion (FWI) can produce high-resolution images of the Earth’s subsurface. Since full-waveform modelling is significantly nonlinear with respect to velocities, Monte Carlo methods have been used to assess image uncertainties. However, because of the high computational cost of Monte Carlo sampling methods, uncertainty assessment remains intractable for larger data sets and 3-D applications. In this study, we propose a new method called variational FWI, which uses Stein variational gradient descent to solve FWI problems. We apply the method to a 2-D synthetic exa

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15

van Herwaarden, Dirk Philip, Michael Afanasiev, Solvi Thrastarson, and Andreas Fichtner. "Evolutionary full-waveform inversion." Geophysical Journal International 224, no. 1 (2020): 306–11. http://dx.doi.org/10.1093/gji/ggaa459.

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SUMMARY We present a new approach to full-waveform inversion (FWI) that enables the assimilation of data sets that expand over time without the need to reinvert all data. This evolutionary inversion rests on a reinterpretation of stochastic Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS), which randomly exploits redundancies to achieve convergence without ever considering the data set as a whole. Specifically for seismological applications, we consider a dynamic mini-batch stochastic L-BFGS, where the size of mini-batches adapts to the number of sources needed to approximate the compl

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16

Yao, Gang, and Di Wu. "Reflection full waveform inversion." Science China Earth Sciences 60, no. 10 (2017): 1783–94. http://dx.doi.org/10.1007/s11430-016-9091-9.

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17

Sinha, Mrinal, and Gerard T. Schuster. "Interferometric full-waveform inversion." GEOPHYSICS 84, no. 1 (2019): R45—R60. http://dx.doi.org/10.1190/geo2018-0047.1.

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Velocity errors in the shallow part of the velocity model can lead to erroneous estimates of the full-waveform inversion (FWI) tomogram. If the location and topography of a reflector are known, then such a reflector can be used as a reference reflector to update the underlying velocity model. Reflections corresponding to this reference reflector are windowed in the data space. Windowed reference reflections are then crosscorrelated with reflections from deeper interfaces, which leads to partial cancellation of static errors caused by the overburden above the reference interface. Interferometri

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18

Zhang, Chao, Ting Lei, and Yi Wang. "Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios." Applied Sciences 11, no. 15 (2021): 6712. http://dx.doi.org/10.3390/app11156712.

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Surface-wave dispersion and the Z/H ratio are important parameters used to resolve the Earth’s structure, especially for S-wave velocity. Several previous studies have explored using joint inversion of these two datasets. However, all of these studies used a 1-D depth-sensitivity kernel, which lacks precision when the structure is laterally heterogeneous. Adjoint tomography (i.e., full-waveform inversion) is a state-of-the-art imaging method with a high resolution. It can obtain better-resolved lithospheric structures beyond the resolving ability of traditional ray-based travel-time tomography

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19

Pan, Guangdong, Lin Liang, and Tarek M. Habashy. "A numerical study of 3D frequency-domain elastic full-waveform inversion." GEOPHYSICS 84, no. 1 (2019): R99—R108. http://dx.doi.org/10.1190/geo2017-0727.1.

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We have developed a 3D elastic full-waveform inversion (FWI) algorithm with forward modeling and inversion performed in the frequency domain. The Helmholtz equation is solved with a second-order finite-difference method using an iterative solver equipped with an efficient complex-shifted incomplete LU-based preconditioner. The inversion is based on the minimization of the data misfit functional and a total variation regularization for the unknown model parameters. We implement the Gauss-Newton method as the optimization engine for the inversions. The codes are parallelized with a message passi

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20

Pratt, R. Gerhard. "Seismic waveform inversion in the frequency domain, Part 1: Theory and verification in a physical scale model." GEOPHYSICS 64, no. 3 (1999): 888–901. http://dx.doi.org/10.1190/1.1444597.

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Seismic waveforms contain much information that is ignored under standard processing schemes; seismic waveform inversion seeks to use the full information content of the recorded wavefield. In this paper I present, apply, and evaluate a frequency‐space domain approach to waveform inversion. The method is a local descent algorithm that proceeds from a starting model to refine the model in order to reduce the waveform misfit between observed and model data. The model data are computed using a full‐wave equation, viscoacoustic, frequency‐domain, finite‐difference method. Ray asymptotics are avoid

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21

Ha, Wansoo, and Changsoo Shin. "Why do Laplace-domain waveform inversions yield long-wavelength results?" GEOPHYSICS 78, no. 4 (2013): R167—R173. http://dx.doi.org/10.1190/geo2012-0365.1.

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Laplace-domain inversions generate long-wavelength velocity models from synthetic and field data sets, unlike full-waveform inversions in the time or frequency domain. By examining the gradient directions of Laplace-domain inversions, we explain why they result in long-wavelength velocity models. The gradient direction of the inversion is calculated by multiplying the virtual source and the back-propagated wavefield. The virtual source has long-wavelength features because it is the product of the smooth forward-modeled wavefield and the partial derivative of the impedance matrix, which depends

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22

Lyu, Chao, Yann Capdeville, David Al-Attar, and Liang Zhao. "Intrinsic non-uniqueness of the acoustic full waveform inverse problem." Geophysical Journal International 226, no. 2 (2021): 795–802. http://dx.doi.org/10.1093/gji/ggab134.

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SUMMARY In the context of seismic imaging, full waveform inversion (FWI) is increasingly popular. Because of its lower numerical cost, the acoustic approximation is often used, especially at the exploration geophysics scale, both for tests and for real data. Moreover, some research domains such as helioseismology face true acoustic media for which FWI can be useful. In this work, an argument that combines particle relabelling and homogenization is used to show that the general acoustic inverse problem based on band-limited data is intrinsically non-unique. It follows that the results of such i

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van Herwaarden, Dirk Philip, Christian Boehm, Michael Afanasiev, et al. "Accelerated full-waveform inversion using dynamic mini-batches." Geophysical Journal International 221, no. 2 (2020): 1427–38. http://dx.doi.org/10.1093/gji/ggaa079.

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SUMMARY We present an accelerated full-waveform inversion based on dynamic mini-batch optimization, which naturally exploits redundancies in observed data from different sources. The method rests on the selection of quasi-random subsets (mini-batches) of sources, used to approximate the misfit and the gradient of the complete data set. The size of the mini-batch is dynamically controlled by the desired quality of the gradient approximation. Within each mini-batch, redundancy is minimized by selecting sources with the largest angular differences between their respective gradients, and spatial c

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Gao, Guozhong, Aria Abubakar, and Tarek M. Habashy. "Joint petrophysical inversion of electromagnetic and full-waveform seismic data." GEOPHYSICS 77, no. 3 (2012): WA3—WA18. http://dx.doi.org/10.1190/geo2011-0157.1.

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Accurate determination of reservoir petrophysical parameters is of great importance for reservoir monitoring and characterization. We developed a joint inversion approach for the direct estimation of in situ reservoir petrophysical parameters such as porosity and fluid saturations by jointly inverting electromagnetic and full-waveform seismic measurements. Full-waveform seismic inversions allow the exploitation of the full content of the data so that a more accurate geophysical model can be inferred. Electromagnetic data are linked to porosity and fluid saturations through Archie’s equations,

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Li, Jiacheng, Xiao He, Jiawei Wen, and Jixin Yang. "Near-borehole imaging with dipole acoustic logging based on full waveform inversion." Journal of the Acoustical Society of America 158, no. 1 (2025): 576–89. https://doi.org/10.1121/10.0037222.

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In acoustic logging, the dipole-mode wave is a type of guided wave that propagates along the borehole, and its dispersion characteristics are typically used to invert the shear-wave velocity of the formation around the borehole. However, traditional dispersion-based inversions rely on the layered model assumption, limiting their applicability to formations that are either uniformly distributed along the borehole axis or exhibit gradual variations. As a method that directly fits observed waveforms, full waveform inversion (FWI) can be applied to various formation models without being constraine

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Moghaddam, Peyman P., Henk Keers, Felix J. Herrmann, and Wim A. Mulder. "A new optimization approach for source-encoding full-waveform inversion." GEOPHYSICS 78, no. 3 (2013): R125—R132. http://dx.doi.org/10.1190/geo2012-0090.1.

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Waveform inversion is the method of choice for determining a highly heterogeneous subsurface structure. However, conventional waveform inversion requires that the wavefield for each source is computed separately. This makes it very expensive for realistic 3D seismic surveys. Source-encoding waveform inversion, in which the sources are modeled simultaneously, is considerably faster than conventional waveform inversion but suffers from artifacts. These artifacts can partly be removed by assigning random weights to the source wavefields. We found that the misfit function, and therefore also its g

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Habashy, T. M., A. Abubakar, G. Pan, and A. Belani. "Source-receiver compression scheme for full-waveform seismic inversion." GEOPHYSICS 76, no. 4 (2011): R95—R108. http://dx.doi.org/10.1190/1.3590213.

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We have developed a source-receiver compression approach for reducing the computational time and memory usage of the acoustic and elastic full-waveform inversions. By detecting and quantifying the extent of redundancy in the data, we assembled a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers used in the survey. Because the numbers of these simultaneous sources and receivers could be significantly less than those of the physical sources and receivers, the computational time and memory usage of any gradient-type inversion method suc

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Cao, D., W. B. Beydoun, S. C. Singh, and A. Tarantola. "A simultaneous inversion for background velocity and impedance maps." GEOPHYSICS 55, no. 4 (1990): 458–69. http://dx.doi.org/10.1190/1.1442855.

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Full‐waveform inversion of seismic reflection data is highly nonlinear because of the irregular form of the function measuring the misfit between the observed and the synthetic data. Since the nonlinearity results mainly from the parameters describing seismic velocities, an alternative to the full nonlinear inversion is to have an inversion method which remains nonlinear with respect to velocities but linear with respect to impedance contrasts. The traditional approach is to decouple the nonlinear and linear parts by first estimating the background velocity from traveltimes, using either trave

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Romanowicz, Barbara, Li-Wei Chen, and Scott W. French. "Accelerating full waveform inversion via source stacking and cross-correlations." Geophysical Journal International 220, no. 1 (2019): 308–22. http://dx.doi.org/10.1093/gji/ggz437.

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SUMMARY Accurate synthetic seismic wavefields can now be computed in 3-D earth models using the spectral element method (SEM), which helps improve resolution in full waveform global tomography. However, computational costs are still a challenge. These costs can be reduced by implementing a source stacking method, in which multiple earthquake sources are simultaneously triggered in only one teleseismic SEM simulation. One drawback of this approach is the perceived loss of resolution at depth, in particular because high-amplitude fundamental mode surface waves dominate the summed waveforms, with

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Yuan, Shihao, Nobuaki Fuji, and Satish C. Singh. "High-frequency localized elastic full-waveform inversion for time-lapse seismic surveys." GEOPHYSICS 86, no. 3 (2021): R277—R292. http://dx.doi.org/10.1190/geo2020-0286.1.

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Seismic full-waveform inversion (FWI) is a powerful method used to estimate the elastic properties of the subsurface. To mitigate the nonlinearity and cycle-skipping problems, in a hierarchical manner, one first inverts the low-frequency content to determine long- and medium-wavelength structures and then increases the frequency content to obtain detailed information. However, the inversion of higher frequencies can be computationally very expensive, especially when the target of interest, such as oil/gas reservoirs and axial melt lens, is at a great depth, far away from source and receiver ar

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31

Zhu, Weiqiang, Kailai Xu, Eric Darve, Biondo Biondi, and Gregory C. Beroza. "Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification." GEOPHYSICS 87, no. 1 (2021): R93—R109. http://dx.doi.org/10.1190/geo2020-0933.1.

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Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity m

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Dantas, Renato R. S., Walter E. Medeiros, and Jessé C. Costa. "A multiscale approach to full-waveform inversion using a sequence of time-domain misfit functions." GEOPHYSICS 84, no. 4 (2019): R539—R551. http://dx.doi.org/10.1190/geo2018-0291.1.

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Most of the approaches designed to avoid cycle skipping in full-waveform inversion (FWI) involve calculating a sequence of inversions in a multiscale fashion. We have adopted an alternative strategy, which is inverting a sequence of different misfit functions in the time domain. This is an implicit multiscale approach in the sense that the used misfit functions are sensitive to different wavelengths, but all of the inversion steps use the same modeling algorithm and the same model grid. In the first and third inversion steps, the transmitted (early arrivals) and reflected (late arrivals) compo

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Cheng, C. H. "Full waveform inversion ofPwaves forVsandQp." Journal of Geophysical Research: Solid Earth 94, B11 (1989): 15619–25. http://dx.doi.org/10.1029/jb094ib11p15619.

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Qu, Yingming, Zhenchun Li, Jianping Huang, and Jinli Li. "Viscoacoustic anisotropic full waveform inversion." Journal of Applied Geophysics 136 (January 2017): 484–97. http://dx.doi.org/10.1016/j.jappgeo.2016.12.001.

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He, Bin, Yike Liu, Huiyi Lu, and Zhendong Zhang. "Correlative Full-Intensity Waveform Inversion." IEEE Transactions on Geoscience and Remote Sensing 58, no. 10 (2020): 6983–94. http://dx.doi.org/10.1109/tgrs.2020.2978433.

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Warner, Michael, Andrew Ratcliffe, Tenice Nangoo, et al. "Anisotropic 3D full-waveform inversion." GEOPHYSICS 78, no. 2 (2013): R59—R80. http://dx.doi.org/10.1190/geo2012-0338.1.

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We have developed and implemented a robust and practical scheme for anisotropic 3D acoustic full-waveform inversion (FWI). We demonstrate this scheme on a field data set, applying it to a 4C ocean-bottom survey over the Tommeliten Alpha field in the North Sea. This shallow-water data set provides good azimuthal coverage to offsets of 7 km, with reduced coverage to a maximum offset of about 11 km. The reservoir lies at the crest of a high-velocity antiformal chalk section, overlain by about 3000 m of clastics within which a low-velocity gas cloud produces a seismic obscured area. We inverted on

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da Silva, Nuno V., Gang Yao, and Michael Warner. "Semiglobal viscoacoustic full-waveform inversion." GEOPHYSICS 84, no. 2 (2019): R271—R293. http://dx.doi.org/10.1190/geo2017-0773.1.

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Full-waveform inversion deals with estimating physical properties of the earth’s subsurface by matching simulated to recorded seismic data. Intrinsic attenuation in the medium leads to the dispersion of propagating waves and the absorption of energy — media with this type of rheology are not perfectly elastic. Accounting for that effect is necessary to simulate wave propagation in realistic geologic media, leading to the need to estimate intrinsic attenuation from the seismic data. That increases the complexity of the constitutive laws leading to additional issues related to the ill-posed natu

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Tao, Kai, Stephen P. Grand, and Fenglin Niu. "Full-waveform inversion of triplicated data using a normalized-correlation-coefficient-based misfit function." Geophysical Journal International 210, no. 3 (2017): 1517–24. http://dx.doi.org/10.1093/gji/ggx249.

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Summary In seismic full-waveform inversion (FWI), the choice of misfit function determines what information in data is used and ultimately affects the resolution of the inverted images of the Earth's structure. Misfit functions based on traveltime have been successfully applied in global and regional tomographic studies. However, wave propagation through the upper mantle results in multiple phases arriving at a given receiver in a narrow time interval resulting in complicated waveforms that evolve with distance. To extract waveform information as well as traveltime, we use a misfit function ba

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Garg, Aayush, and D. J. Verschuur. "From surface seismic data to reservoir elastic parameters using a full-wavefield redatuming approach." Geophysical Journal International 221, no. 1 (2019): 115–28. http://dx.doi.org/10.1093/gji/ggz557.

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SUMMARY Traditionally, reservoir elastic parameters inversion suffers from the overburden multiple scattering and transmission imprint in the local input data used for the target-oriented inversion. In this paper, we present a full-wavefield approach, called reservoir-oriented joint migration inversion (JMI-res), to estimate the high-resolution reservoir elastic parameters from surface seismic data. As a first step in JMI-res, we reconstruct the fully redatumed data (local impulse responses) at a suitable depth above the reservoir from the surface seismic data, while correctly accounting for t

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Borisov, Dmitry, Fuchun Gao, Paul Williamson, and Jeroen Tromp. "Application of 2D full-waveform inversion on exploration land data." GEOPHYSICS 85, no. 2 (2020): R75—R86. http://dx.doi.org/10.1190/geo2019-0082.1.

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Estimating subsurface seismic properties is an important topic in civil engineering, oil and gas exploration, and global seismology. We have developed an application of 2D elastic waveform inversion with an active-source on-shore data set, as is typically acquired in exploration seismology on land. The maximum offset is limited to 12 km, and the lowest available frequency is 5 Hz. In such a context, surface waves are generally treated as noise and are removed as a part of data processing. In contrast to the conventional approach, our workflow starts by inverting surface waves to constrain shal

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Pérez Solano, Carlos, and René-Édouard Plessix. "Velocity-model building with enhanced shallow resolution using elastic waveform inversion — An example from onshore Oman." GEOPHYSICS 84, no. 6 (2019): R977—R988. http://dx.doi.org/10.1190/geo2018-0736.1.

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Full-waveform inversion is a powerful data-fitting technique that is used for velocity-model building in seismic exploration. The inversion approach exploits the sensitivity of long-offset, wide-aperture, low-frequency data to the P-wave velocity properties in the subsurface. In the geologically complex land context in which different lithologies interleave and create large elastic property contrasts, acoustic waveform inversion is challenged due to the elastic nature of the data. The large elastic property contrasts create mode conversions. At low-to-intermediate frequencies, due to tuning/in

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Métivier, L., R. Brossier, and J. Virieux. "Combining asymptotic linearized inversion and full waveform inversion." Geophysical Journal International 201, no. 3 (2015): 1682–703. http://dx.doi.org/10.1093/gji/ggv106.

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Vigh, Denes, and E. William Starr. "3D prestack plane-wave, full-waveform inversion." GEOPHYSICS 73, no. 5 (2008): VE135—VE144. http://dx.doi.org/10.1190/1.2952623.

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Prestack depth migration has been used for decades to derive velocity distributions in depth. Numerous tools and methodologies have been developed to reach this goal. Exploration in geologically more complex areas exceeds the abilities of existing methods. New data-acquisition and data-processing methods are required to answer these new challenges effectively. The recently introduced wide-azimuth data acquisition method offers better illumination and noise attenuation as well as an opportunity to more accurately determine velocities for imaging. One of the most advanced tools for depth imaging

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Barnes, Christophe, and Marwan Charara. "The domain of applicability of acoustic full-waveform inversion for marine seismic data." GEOPHYSICS 74, no. 6 (2009): WCC91—WCC103. http://dx.doi.org/10.1190/1.3250269.

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Marine reflection seismic data inversion is a compute-intensive process, especially in three dimensions. Approximations often are made to limit the number of physical parameters we invert for, or to speed up the forward modeling. Because the data often are dominated by unconverted P-waves, one popular approximation is to consider the earth as purely acoustic, i.e., no shear modulus. The material density sometimes is taken as a constant. Nonlinear waveform seismic inversion consists of iteratively minimizing the misfit between the amplitudes of the measured and the modeled data. Approximations,

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Zhao, Xinglei, Jianfei Gao, Hui Xia, and Fengnian Zhou. "Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR." Sensors 22, no. 24 (2022): 10005. http://dx.doi.org/10.3390/s222410005.

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In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified usin

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Warner, Michael, and Lluís Guasch. "Adaptive waveform inversion: Theory." GEOPHYSICS 81, no. 6 (2016): R429—R445. http://dx.doi.org/10.1190/geo2015-0387.1.

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Conventional full-waveform seismic inversion attempts to find a model of the subsurface that is able to predict observed seismic waveforms exactly; it proceeds by minimizing the difference between the observed and predicted data directly, iterating in a series of linearized steps from an assumed starting model. If this starting model is too far removed from the true model, then this approach leads to a spurious model in which the predicted data are cycle skipped with respect to the observed data. Adaptive waveform inversion (AWI) provides a new form of full-waveform inversion (FWI) that appear

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Biondi, Biondo, and Ali Almomin. "Simultaneous inversion of full data bandwidth by tomographic full-waveform inversion." GEOPHYSICS 79, no. 3 (2014): WA129—WA140. http://dx.doi.org/10.1190/geo2013-0340.1.

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The convergence of full-waveform inversion can be improved by extending the velocity model along either the subsurface-offset axis or the time-lag axis. The extension of the velocity model along the time-lag axis enables us to linearly model large time shifts caused by velocity perturbations. This linear modeling was based on a new linearization of the scalar wave equation in which perturbation of the extended slowness squared was convolved in time with the second time derivative of the background wavefield. The linearization was accurate for reflected events and transmitted events. We determi

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Hornby, Brian E. "Tomographic reconstruction of near‐borehole slowness using refracted borehole sonic arrivals." GEOPHYSICS 58, no. 12 (1993): 1726–38. http://dx.doi.org/10.1190/1.1443387.

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Two‐dimensional (2-D) reconstructions of the near‐borehole slowness field are computed using arrival times of refracted borehole sonic arrivals. First‐arrival traveltimes, derived from both computer simulations and field data from full‐waveform sonic tools, were inverted for the near‐borehole formation slowness both axially along the borehole and radially away from the borehole. The inversion is nonlinear; the solution is obtained by means of a series of linear inversions followed by provisional ray tracings. Each iteration involves the application of a tomographic reconstruction algorithm sim

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Manukyan, Edgar, Hansruedi Maurer, and André Nuber. "Improvements to elastic full-waveform inversion using cross-gradient constraints." GEOPHYSICS 83, no. 2 (2018): R105—R115. http://dx.doi.org/10.1190/geo2017-0266.1.

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Seismic full-waveform inversion (FWI) is potentially a powerful method for obtaining high-resolution subsurface images, but the results are often distorted by nonlinear effects and parameter trade-offs. Such distortions can be particularly severe in the case of multiparameter FWI, such as elastic FWI, in which inversion is performed simultaneously for P- and S-wave velocities and density. The problem can be alleviated by adding constraints in the form of plausible a priori information. A usually well-justified constraint includes the structural similarity of different model parameters; i.e., a

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Guitton, Antoine, Gboyega Ayeni, and Esteban Díaz. "Constrained full-waveform inversion by model reparameterization." GEOPHYSICS 77, no. 2 (2012): R117—R127. http://dx.doi.org/10.1190/geo2011-0196.1.

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The waveform inversion problem is inherently ill-posed. Traditionally, regularization schemes are used to address this issue. For waveform inversion, where the model is expected to have many details reflecting the physical properties of the Earth, regularization and data fitting can work in opposite directions: the former smoothing and the latter adding details to the model. We propose constraining estimated velocity fields by reparameterizing the model. This technique, also called model-space preconditioning, is based on directional Laplacian filters: It preserves most of the details of the v

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