High-performance 3D first-arrival traveltime tomography

Noble, Mark; Thierry, Philippe; Taillandier, Cédric; Calandra, Henri

doi:10.1190/1.3284057

Cited by 24 publications

(6 citation statements)

References 5 publications

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“…This avoids ray tracing and the explicit calculation of the Fréchet derivative matrix. It has relatively minimal memory requirements and is particularly suitable for parallel computing (Benaichouche et al., 2015; Noble et al., 2010). Due to these advantages, the AST method recently has gained much attention in velocity model building in both seismic exploration and the Earth internal structure study (Huang & Bellefleur, 2012; Han et al., 2019; Li et al., 2014; Li et al., 2017; Tong, 2021; Waheed et al., 2016a, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Preconditioned transmission + reflection joint traveltime tomography with adjoint‐state method for subsurface velocity model building

Zhang

Dong

Wang

et al. 2022

Geophysical Prospecting

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Adjoint-state-based transmission and reflection traveltime inversion are promising approaches for reconstructing a macro velocity field in both shallow and deep depths. However, the contaminated gradient by the uneven illumination impedes the recovery of an optimal velocity model and results in slow convergence rate in inversion. To mitigate this issue, we propose a novel preconditioned transmission + reflection joint traveltime tomography method with transmission and reflection illumination compensation. The proposed illumination compensation operator is theoretically demonstrated as the diagonal elements of the approximated Hessian, which can be efficiently computed in a way similar to gradient calculation. The proposed method can construct a more accurate velocity model compared to the uncompensated counterpart. Convergence is also accelerated by the relatively uniform velocity updates from the shallow to the deep parts. Furthermore, velocity updating and reflector imaging are simultaneously iterated during the inversion. Synthetic numerical example based on the inclusion model indicates that the proposed preconditioned method can result in uniform gradient values of both transmitted and shot/receiver-side reflected traveltime. The complex foothill model test shows the improved inversion accuracy of the proposed method with an increased convergence rate and improved reflection traveltime predictions using the inverted velocity model. Experiment on the small foothill model indicates that the reflector and velocity can be updated simultaneously, and the inversion accuracy can be significantly improved by selecting multi reflectors. Finally, the effectiveness and practicality of the proposed method are demonstrated by a field data application in East China Sea.

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Section: Introductionmentioning

confidence: 99%

Preconditioned transmission + reflection joint traveltime tomography with adjoint‐state method for subsurface velocity model building

Zhang

Dong

Wang

et al. 2022

Geophysical Prospecting

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“…; Noble et al . ) making them suitable for large‐scale optimization problems (i.e. three‐dimensional velocity models containing millions of parameters whatever the type of parameterization).…”

Section: Introductionmentioning

confidence: 99%

“…From a computational point of view, derivative-based methods are very attractive since the gradient can be efficiently calculated using the adjoint-state method (e.g. Taillandier et al 2009;Noble et al 2010) making them suitable for large-scale optimization problems (i.e. three-dimensional velocity models containing millions of parameters whatever the type of parameterization).…”

Section: Introductionmentioning

confidence: 99%

Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms

Luu

Noble

Gesret

et al. 2019

Geophysical Prospecting

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The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non‐linear and multi‐modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative‐based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative‐based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population‐based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm.

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“…The first-break traveltime tomography (Zhu et al, 1992;Osypov, 2000;Leung and Qian, 2006;Taillandier et al, 2009;Noble et al, 2010) is an established tool for estimating near-surface macrofeature seismic velocities. Starting from a prior model, tomographic inversion gradually modifies the velocities such that the misfits between predicted and observed first breaks decrease.…”

Section: Introductionmentioning

confidence: 99%

First-break traveltime tomography with the double-square-root eikonal equation

Vladimirsky

Fomel

2013

GEOPHYSICS

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First-break traveltime tomography is based on the eikonal equation. Because the eikonal equation is solved at fixed-shot positions and only receiver positions can move along the raypath, the adjoint-state tomography relies on inversion to resolve possible contradicting information between independent shots. The double-square-root (DSR) eikonal equation allows not only the receivers but also the shots to change position, and thus describes the prestack survey as a whole. Consequently, its linearized tomographic operator naturally handles all shots together, in contrast with the shotwise approach in the traditional eikonalbased framework. The DSR eikonal equation is singular for the horizontal waves, which require special handling. Although it is possible to recover all branches of the solution through postprocessing, our current forward modeling and tomography focuses on the diving wave branch only. We consider two upwind discretizations of the DSR eikonal equation and show that the explicit scheme is only conditionally convergent and relies on nonphysical stability conditions. We then prove that an implicit upwind discretization is unconditionally convergent and monotonically causal. The latter property makes it possible to introduce a modified fast matching method thus obtaining first-break traveltimes efficiently and accurately. To compare the new DSR eikonal-based tomography and traditional eikonal-based tomography, we perform linearizations and apply the same adjoint-state formulation and upwind finite-differences implementation to both approaches. Synthetic model examples justify that the proposed approach converges faster and is more robust than the traditional one.

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High-performance 3D first-arrival traveltime tomography

Cited by 24 publications

References 5 publications

Preconditioned transmission + reflection joint traveltime tomography with adjoint‐state method for subsurface velocity model building

Preconditioned transmission + reflection joint traveltime tomography with adjoint‐state method for subsurface velocity model building

Towards large‐scale stochastic refraction tomography: a comparison of three evolutionary algorithms

First-break traveltime tomography with the double-square-root eikonal equation

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