Full-bandwidth adaptive waveform inversion at the reservoir

Debens, H.; Mancini, Fabio; Warner, Mike; Guasch, Lluís

doi:10.1190/segam2017-17700260.1

Cited by 7 publications

(4 citation statements)

References 5 publications

(4 reference statements)

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“…Facing challenging field data applications with alternative misfit functions is not widely documented in the literature. One of the only other alternative misfit functions that has been applied successfully to field data is adaptive waveform inversion (AWI) (Warner and Guasch, 2015;Ravaut et al, 2017;Debens et al, 2017;Roth et al, 2018;Guasch et al, 2019;Warner et al, 2019) or Kantorovich-Rubinstein optimal transport (KROT) (Poncet et al, 2018;Messud and Sedova, 2019;Sedova et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

et al. 2022

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Improving full-waveform inversion to make it more robust to cycle-skipping has been the subject of a large number of studies. From the several families of approaches developed, one of the most documented consists in modifying the least-squares distance defining the discrepancy between observed and calculated data. From all the propositions made to improve and replace the least-squares distance, only a few of them have been applied to field data. One of the methods proposed recently, the graph space optimal transport distance, presents appealing properties for field data applications. This study compares it with the least-squares distance in an analysis performed on the three-dimensional ocean bottom cable data from the Valhall field. This data has already been at the heart of several full-waveform inversion studies, making it a good candidate to evaluate the properties of this new misfit function. We first perform this comparison starting the inversion from the reflection traveltime tomography model used in previous studies. We then perform a second comparison from a crude, linearly varying in-depth one-dimensional velocity model. Starting from this model, least-squares-based full-waveform inversion fails to provide a meaningful estimate of the pressure-wave velocity model due to cycle skipping. We illustrate how the graph-space optimal transport-based full-waveform inversion mitigates this issue. A meaningful estimate of the pressure-wave velocity model is obtained in the zone sampled by both diving and reflected waves, down to almost two kilometers depth. To our knowledge, this is the first application of a graph space optimal transport-based full-waveform inversion to three-dimensional field data.

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Section: Discussionmentioning

confidence: 99%

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

et al. 2022

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“…We point out that other meta-heuristic methods could have been chosen as for example genetic algorithms (GA) (Sen and Stoffa 2013). However, it was reported higher efficiency (Debens 2015) and suitability for continuous optimization (Kachitvichyanukul 2012) of QPSO over GA. QPSO belongs to a class of meta-heuristic optimization algorithms known as particle-swarm optim ization (Kennedy and Eberhart 1995). The latter emulates the dynamics of a swarm of particles ruled by the laws of classical mechanics.…”

Section: Quantum Particle Swarm Optimizationmentioning

confidence: 99%

Semi-global inversion of v _p to v _s ratio for elastic wavefield inversion

2018

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We introduce an approach to estimate the ratio between P-and S-wave velocities, v p /v s , in the scope of elastic full waveform inversion (FWI). Elastic FWI is generally implemented with local optimization methods relying on initial estimates of the long wavelengths of P-and S-wave models. However, successful inversions can be hindered if an accurate enough relation between v p and v s velocities is not used as a constraint. This relation can be estimated from empirical relations. Herein, we introduce an alternative approach based upon a semi-global inversion scheme. We observe that for a large number of cases, and particularly in the context of FWI, v p /v s can be represented on a sparse basis. This sparse basis has a much smaller dimension than that of the typical model space in elastic FWI. This creates the possibility of using global optimization methods. The optimal estimate of v p /v s is obtained with quantum particle swarm optimization (QPSO). This method probes a population of possible models. The assessment of each model of v p /v s in the population is obtained with nested local iterations updating for v p only. Conventional elastic FWI is then carried out for jointly estimating high-resolution models of v p and v s . We demonstrate with synthetic examples that the estimates of v p are relatively robust to errors in the estimated v p /v s , and that effectively a sparse representation of the model of v p /v s is feasible for the reconstruction of a model of v s . We also demonstrate that the proposed approach performs better than constraining elastic FWI with an empirical relation between v p and v s , leading to improved estimates of models of v p and v s from seismic data.

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“…As a result, the application of the global comparison methods, like the matching-filter and the optimal-transport methods, require some preprocessing and conditioning. For example, Huang et al (2017) reported that the matching-filter approach deals mainly with the diving waves and surfers from strong multiples (Debens et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Robust full-waveform inversion with Radon-domain matching filter

Sun

Alkhalifah

2019

GEOPHYSICS

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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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Full-bandwidth adaptive waveform inversion at the reservoir

Cited by 7 publications

References 5 publications

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

Semi-global inversion of v _p to v _s ratio for elastic wavefield inversion

Robust full-waveform inversion with Radon-domain matching filter

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Full-bandwidth adaptive waveform inversion at the reservoir

Cited by 7 publications

References 5 publications

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

Robust full-waveform inversion with graph-space optimal transport: Application to 3D ocean-bottom cable Valhall data

Semi-global inversion of v p to v s ratio for elastic wavefield inversion

Robust full-waveform inversion with Radon-domain matching filter

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Product

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Semi-global inversion of v _p to v _s ratio for elastic wavefield inversion