Seismic Acoustic Impedance Inversion Using Mixed Second-Order Fractional ATpV Regularization

Wu, Hao; He, Yejun; Chen, Yingpin; Li, Shu; Peng, Zhenming

doi:10.1109/access.2019.2962552

Cited by 16 publications

(3 citation statements)

References 51 publications

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“…Common methods for removing salt and pepper noise include median-filter-based methods [7][8][9][10][11][12][13][14][15][16], total variation (TV)-based methods [17][18][19][20][21], Euler's elastica variational method [22], deep-learning-based methods [23][24][25], and wavelet transform (WT)-based methods [26]. Median-filter-based methods replace the noise pixel with the median value of adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Chen

Huang

Wang

et al. 2022

IET Image Processing

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Salt and pepper noise occurs randomly and causes image degradation. Numerous denoising methods have been proposed to suppress this noise. However, existing methods have two main limitations. First, noise characteristics, such as noise location information and sparsity, are often described inaccurately or even ignored. Second, many existing methods separate the contaminated image into a recovered image and a noise part, leading to the recovery of an image with unsatisfactory smooth and detailed parts. In this study, the authors introduce a noise detection strategy to determine the position of the noise and employ a non-convex sparsity regularization depicted by l p quasi-norm to describe the sparsity of the noise, thereby addressing the first limitation. We adopt the morphological component analysis framework with stationary Framelet transform to decompose the processed image into the cartoon, texture, and noise parts to resolve the second limitation. Then, the proposed model is applied by using the alternating direction method of multipliers (ADMM). Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image and outperforming some state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Chen

Huang

Wang

et al. 2022

IET Image Processing

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“…The well‐logging constrained method (Carron, 1989; Vest, 1990) supplements the limited bandwidth of seismic data by utilizing well logs with rich high‐frequency components and low‐frequency components as a priori information (Li et al ., 2021). The relationship between seismic data and impedance is complex and nonlinear; therefore, traditional inversion methods need to design a complex optimization system (de Figueiredo et al ., 2014; Bordignon et al ., 2017; Wu et al ., 2019a). Moreover, all the above methods have certain assumptions, and the inversion results will contain significant errors if the preconditions are not met.…”

Section: Introductionmentioning

confidence: 99%

Seismic impedance inversion using a multi‐input neural network with a two‐step training strategy

Meng

Wang

Niu

et al. 2022

Geophysical Prospecting

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Deep learning has shown excellent performance in simulating complex nonlinear mappings from the seismic data to elastic parameters. However, seismic acoustic impedance estimated from a direct mapping from seismic waveform data to P‐wave impedance (single‐input network) is hampered by the limited frequency bands. In this paper, we propose to incorporate the low‐frequency impedance model to constrain the inversion (multi‐input network). We add a feature fusion layer to force the lateral smoothness. Besides, usually, a given seismic survey is likely to contain only a few well logs, which is insufficient for conventional deep‐learning ‐based methods to learn the complex mapping from seismic data to elastic parameters. The problem is compounded by the fact that a network trained with synthetic data (compensated for the lack of logs) cannot be directly used for field data. Therefore, we propose to use transfer learning to mitigate this issue. The multi‐input neural network is trained using synthetics and real data in two stages. We carry out experiments to demonstrate that the two‐step training multi‐input network approach has high accuracy in the time direction, excellent continuity in the lateral direction and favourable robustness. Synthetic and field data examples demonstrate that the proposed network can accurately predict impedance even with limited logging data, which provides a reference for oil and gas exploration in the actual production process.

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“…Wang, 2010) and as an anti-noise functional (C. Liu et al, 2015). Total variation (T V −norm) has been successfully used to promote sparsity of the reflection coefficients (F. Zhang et al, 2014) and acoustic impedance (Wu, He, et al, 2019). Zand et al (2020 used the T V − norm regularization to obtain a stable solution of the least-square reverse-time migration (LSRTM).…”

Section: Introductionmentioning

confidence: 99%

AVA Inversion of PP Reflection in a VTI Medium using Proximal Splitting Algorithm

Zidan

Cheng

2022

Preprint

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Amplitude variation with offset (AVO) inversion, particularly for more than two model parameters, is a highly ill-posed problem and, hence, regularization is indispensable. Here, we propose a regularized inverse problem to mitigate the ill-posedness of the amplitude inversion. The regularization is added to measure the difference in information between the a priori probability density function and the predicted probability density of the inverted parameters. Information theory provides a collection of contrast functions which quantify the divergence from one probability distribution to another, such as the relative entropy. The a priori density is approximated by a Gaussian mixture model, obtained from well logs and rock physics model. The mixture model is a density estimator, providing the statistical properties of the model parameters of interest. The likelihood of the data and the divergence are combined in an augmented Lagrangian scheme, the alternating direction method of multipliers (ADMM), to obtain a unique solution that best generate the recorded seismic data and satisfy the geological constraints conveyed by the a priori probability density function. The proposed inversion scheme is then applied to the anisotropy AVO inversion, for estimating the elastic and seismic anisotropy parameters of shale formations.

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Seismic Acoustic Impedance Inversion Using Mixed Second-Order Fractional ATpV Regularization

Cited by 16 publications

References 51 publications

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Seismic impedance inversion using a multi‐input neural network with a two‐step training strategy

AVA Inversion of PP Reflection in a VTI Medium using Proximal Splitting Algorithm

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