Enhancing resolution in imaging-based velocity estimation using morphological operators

Maciel, Jonathas S.; Costa, João B.; Verschuur, D.J.

doi:10.1190/segam2015-5900483.1

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…Incorporating the information of migration into the tomography process for velocity estimation improves the final depth image (Mathewson et al 2012), where a dirty salt velocity was estimated via the reflectivity, which is computed by true-amplitude RTM, under a 1D assumption (Ji et al 2011). With a similar reasoning, Maciel et al (2015) applied nonlinear filters from the field of morphological image processing to address this challenge and to enhance the contrast of the JMI velocity solution. In order to improve the resolution of the velocity model, we also presented an initial algorithm and result (Masaya & Verschuur 2016) for reflectivityconstrained velocity estimation by adding a slowness-based objective function in JMI, which independently inverts reflectivity and velocity models.…”

Section: Introductionmentioning

confidence: 99%

Iterative reflectivity-constrained velocity estimation for seismic imaging

Masaya

Verschuur

2018

Geophysical Journal International

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This paper proposes a reflectivity constraint for velocity estimation to optimally solve the inverse problem for active seismic imaging. This constraint is based on the velocity model derived from the definition of reflectivity and acoustic impedance. The constraint does not require any prior information of the subsurface and large extra computational costs, like the calculation of so-called Hessian matrices. We incorporate this constraint into the joint migration inversion algorithm, which simultaneously estimates both the reflectivity and velocity model of the subsurface in an iterative process. Using so-called full wavefield modelling, the misfit between forward modelled and measured data is minimized. Numerical and field data examples are given to demonstrate the validity of our proposed algorithm in case accurate initial models and the low-frequency components of observed seismic data are absent.

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Section: Introductionmentioning

confidence: 99%

Iterative reflectivity-constrained velocity estimation for seismic imaging

Masaya

Verschuur

2018

Geophysical Journal International

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A nuclear magnetic resonance echo data filter method based on gray-scale morphology

et al. 2021

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Nuclear magnetic resonance (NMR) echo data measured in the oil field usually have a very low signal-to-noise ratio (SNR). Low SNR of echo data may affect the accuracy of the inversion results, which further lead to the inaccuracy of derived petrophysical parameter estimates. It is therefore important to filter the echo data to enhance the SNR before inversion. Existing filter methods focus on removing noise by compressing the echo data matrix or processing the echo data in time or frequency domain, which are not very efficient and can be affected by artificial interventions. A gray-scale morphology filter method is proposed based on the morphological difference between the echo data and noise. Either elliptical or triangular structure elements can be used for the morphology filter of NMR echo data. The size of the structure elements should be in the range of 1~5 echo spacings to prevent the echo data from being distorted. Comparing the inversion results of the unfiltered, morphology filtered, SVD filtered, and wavelet filtered echo data at different SNRs, the morphology filter method yields the best results at low SNR, and the morphology filter method and the wavelet filter method yield similarly good results at high SNR. The morphology filter method has the shortest run time compared to the SVD method and wavelet filter method. Moreover, this morphology filter method is stable to handle random noise and different T2 distribution models, and it also performs well on NMR well logging data.

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Introduction of the Hessian in joint migration inversion and improved recovery of structural information using image-based regularization

Assis¹,

Schleicher²

2021

GEOPHYSICS

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Joint migration inversion (JMI) is a method based on the one-way wave equations that aims at fitting seismic reflection data to estimate an image and a background velocity. The depth-migrated image describes the high spatial-frequency content of the subsurface and, in principle, is true amplitude. The background velocity model accounts mainly for the large spatial-scale kinematic effects of the wave propagation. Looking for a deeper understanding of the method, we briefly review the continuous equations that compose the forward modeling engine of JMI for acoustic media and angle-independent scattering. Then, we use these equations together with the first-order adjoint-state method to arrive at a new formulation of the model gradients. To estimate the image, we combine the second-order adjoint-state method with the truncated-Newton method to obtain the image updates. For the model (velocity) estimation, in comparison to the image update, we reduce the computational cost by simply adopting a diagonal preconditioner for the corresponding gradient in combination with an image-based regularizing function. Based on this formulation, we build our implementation of the JMI algorithm. The proposed image-based regularization of the model estimate allows us to carry over structural information from the estimated image to the jointly estimated background model. As demonstrated by our numerical experiments, this procedure can help to improve the resolution of the estimated model and make it more consistent with the image.

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Enhancing resolution in imaging-based velocity estimation using morphological operators

Cited by 7 publications

References 9 publications

Iterative reflectivity-constrained velocity estimation for seismic imaging

Iterative reflectivity-constrained velocity estimation for seismic imaging

A nuclear magnetic resonance echo data filter method based on gray-scale morphology

Introduction of the Hessian in joint migration inversion and improved recovery of structural information using image-based regularization

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