Salt Body Inversion Using an Optimal Transport of the Preconditioned Matching Filter

Sun, Bingbing; Alkhalifah, Tariq

doi:10.3997/2214-4609.201900878

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…, W 2 denotes the Wasserstein distance [1]. The resulting OTMF misfit in Equation 3 can overcome the cycle-skipping effectively as demonstrated by [6].…”

Section: A Robust Misfit Function By Optimal Transport Of the Matching Filter (Otmf)mentioning

confidence: 99%

A Data-Driven Choice of Misfit Function for FWI Using Reinforcement Learning

Sun¹,

Alkhalifah²

2020

EAGE 2020 Annual Conference &Amp; Exhibition Online

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In the workflow of Full-Waveform Inversion (FWI), we often tune the parameters of the inversion to help us avoid cycle skipping and obtain high resolution models. For example, typically start by using objective functions that avoid cycle skipping, like tomographic and image based or using only low frequency, and then later, we utilize the least squares misfit to admit high resolution information. We also may perform an isotropic (acoustic) inversion to first update the velocity model and then switch to multi-parameter anisotropic (elastic) inversions to fully recover the complex physics. Such hierarchical approaches are common in FWI, and they often depend on our manual intervention based on many factors, and of course, results depend on experience. However, with the large data size often involved in the inversion and the complexity of the process, making optimal choices is difficult even for an experienced practitioner. Thus, as an example, and within the framework of reinforcement learning, we utilize a deep-Q network (DQN) to learn an optimal policy to determine the proper timing to switch between different misfit functions. Specifically, we train the state-action value function (Q) to predict when to use the conventional L2-norm misfit function or the more advanced optimal-transport matching-filter (OTMF) misfit to mitigate the cycle-skipping and obtain high resolution, as well as improve convergence. We use a simple while demonstrative shifted-signal inversion examples to demonstrate the basic principles of the proposed method.

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“…, W 2 denotes the Wasserstein distance [1]. The resulting OTMF misfit in Equation 3 can overcome the cycle-skipping effectively as demonstrated by [6].…”

Section: A Robust Misfit Function By Optimal Transport Of the Matching Filter (Otmf)mentioning

confidence: 99%

A Data-Driven Choice of Misfit Function for FWI Using Reinforcement Learning

Sun¹,

Alkhalifah²

2020

EAGE 2020 Annual Conference &Amp; Exhibition Online

Self Cite

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“…Conventional industry practice with salt regions is to apply the migrate-pick-flood flow, which is time consuming and prone to error as it requires human interpretation and decision making (Dellinger et al, 2017). For more automatic inversion with less human intervention: Ovcharenko et al (2018) used a variance-based interpolation to process the conventional FWI updates; Sun and Alkhalifah (2019b) proposed to use more robust objective functions such as the optimal-transport when inverting for the salt. Other studies suggested to apply an automatic flooding by constraining the inversion using a Hing loss (Esser et al, 2016) or regularizing it as a post-inversion process (Kalita et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Salt Body Flooding Using Activation Functions From Machine Learning

Alali

Sun

Kazei

et al. 2020

EAGE 2020 Annual Conference &Amp; Exhibition Online

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In salt affected regions, conventional full-waveform inversion (FWI) is doomed to fail if there is no prior information of the salt body. Recent studies suggested to regularize the inversion by implementing an automatic flooding using total variation (TV) and Hinge loss functions. We generlize this approach and introduce a family of functions known as "activation functions" in the machine learning discipline that can be used to implement automatic flooding in similar way. In particular, we investigate the automatic flooding using a sigmoid, tanh and exponential linear unit (Elu) functions and apply them for salt body reconstruction on the BP model and report their performance.

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Joint Minimization of the Mean and Information Entropy of the Matching Filter Distribution for a Robust Misfit Function in Full-Waveform Inversion

Sun

Alkhalifah

2020

IEEE Trans. Geosci. Remote Sensing

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Full-Waveform Inversion (FWI) is a highly nonlinear inversion methodology. FWI tends to converge to a local minimum rather than a global one. We refer to this phenomena as "cycle skipping" in FWI. A cost-effective solution for resolving this issue is to design a more convex misfit function for the optimization problem. A global comparison based on using a matching filter admits more robust misfit functions. In this case, we would compute a matching filter first by deconvolving of the predicted data from the measured ones. When the velocity model is accurate, the predicted data resemble the measured ones, and the resulting matching filter would be an approximated Dirac delta function. If the velocity produces data that are different from the observed ones, a misfit function can be formulated by penalizing the energy away from the zero-lag time (the center). Here, we develop a general mechanism for an evolution of the matching filter to our objective in FWI. Specifically from the statistics point of view, rather than using a penalty, we propose a novel misfit by minimization the mean and information entropy of the matching filter distribution. We show that the resulting misfit function can mitigate the "cycle skipping" as well as reduce the mean and variance of the resulting matching filter distribution. We use a modified Marmousi example to demonstrate the features of the proposed misfit. We also evaluate the robustness of the proposed method using inaccurate (rotation in phase) source wavelets and measured data with different levels of Gaussian random noise.

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Salt Body Inversion Using an Optimal Transport of the Preconditioned Matching Filter

Cited by 7 publications

References 9 publications

A Data-Driven Choice of Misfit Function for FWI Using Reinforcement Learning

A Data-Driven Choice of Misfit Function for FWI Using Reinforcement Learning

Salt Body Flooding Using Activation Functions From Machine Learning

Joint Minimization of the Mean and Information Entropy of the Matching Filter Distribution for a Robust Misfit Function in Full-Waveform Inversion

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