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

Meng, Jinyu; Wang, Shoudong; Niu, Zicheng; Sang, Wenjing; Geng, Weiheng; Cheng, Wanli

doi:10.1111/1365-2478.13229

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…Li Zhongzhi and others designed a CWGAN model combined with convolutional neural networks for bearing fault diagnosis under unbalanced training data [3] . In other applications, Wang Peng et al have provided a seismic inversion technique based on confrontation networks for semi supervised seismic impedance propagation [4] . Song Lijuan proposed a graph text cross retrieval algorithm based on generating adversarial networks [5] .…”

Section: Introductionmentioning

confidence: 99%

Research on fault data enhancement method of smart grid based on generation-countermine neural network

Cao¹,

Qu²,

Zhou³

et al. 2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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In the modern power system, the safe and stable operation of the power transmission network and the special communication network depends on the healthy connection of its supporting network. In order to study and realize the above functions, a large amount of accurate data is very necessary. In this paper, the generation network G (Generator) and the discriminator network D (Discriminator) are used to continuously approach the real partial discharge voltage signal samples. Based on the WGAN model, the auxiliary classifier Wasserstein with gradient penalty is added, and the ACWGAN-GP model is proposed. Through the comparison between the data samples generated by the model and the real data samples, it can be found that there is a relatively consistent fluctuation rule between the two, but the values of the two remain different at the same time, which increases the diversity of the synthetic partial discharge voltage signal samples, is conducive to enhancing the robustness of the partial discharge detection model, and improves its generalization ability to detect faults. This provides a reference for enhancing the fault data of smart grids.

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Section: Introductionmentioning

confidence: 99%

Research on fault data enhancement method of smart grid based on generation-countermine neural network

Cao¹,

Qu²,

Zhou³

et al. 2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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Porosity prediction from prestack seismic data via deep learning: incorporating a low-frequency porosity model

Liu,

Zhao,

et al. 2023

Journal of Geophysics and Engineering

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Porosity prediction from seismic data is of considerable importance in reservoir quality assessment, geological model building, and flow unit delineation. Deep learning approaches have demonstrated great potential in reservoir characterization due to their strong feature extraction and nonlinear relationship mapping abilities. However, the reliability of porosity prediction is often compromised by the lack of low-frequency information in bandlimited seismic data. To address this issue, we propose incorporating a low-frequency porosity model based on geostatistical methodology, into the supervised convolutional neural network to predict porosity from prestack seismic angle gather and seismic inversion results. Our study demonstrates that the inclusion of the low-frequency porosity model significantly improves the reliability of porosity predictions in a heterogeneous carbonate reservoir. The low-frequency information can be compensated to enhance the network's capabilities of capturing the background porosity trend. Additionally, the blind well tests validate that considering the low-frequency constraint leads to stronger model prediction and generalization abilities, with the root mean square error (RMSE) of the two blind wells reduced by up to 34%. The incorporation of the low-frequency reservoir model in network training also remarkably enhances the geological continuity of seismic porosity prediction, providing more geologically reasonable results for reservoir characterization.

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Enhancing seismic porosity estimation through 3D sequence-to-sequence deep learning with data augmentation, spatial constraints, and geologic constraints

Xu,

Zhao,

Liu

et al. 2024

GEOPHYSICS

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Estimating porosity from seismic data is critical for studying underground rock properties, assessing energy reserves, and subsequent reservoir exploration and development. For reservoirs with strong heterogeneity, the endeavor to accurately and stably characterize spatial variations in porosity often encounters considerable challenges due to the rapid lateral changes of formations. In view of this, establishing a robust mapping relationship from seismic data to reservoir properties in three-dimensional (3D) space is important in addressing this challenge. We propose to transform the conventional one-dimensional (1D) sequence-to-point (STP) prediction paradigm into a 3D sequence-to-sequence (STS) prediction paradigm to enable machine learning to extract 3D seismic data features. 3D STS prediction presents valuable potential for enhancing the geological continuity and vertical characterization ability of porosity compared to STP. Building upon the 3D STS prediction model, three strategies from different perspectives are introduced to further enhance the performance of seismic porosity estimation. First, we apply a translation-based data augmentation (DA) strategy to mitigate the problem of sparsely labeled data. Second, we propose spatial constraints (SC) considering absolute coordinates and relative time to boost the spatial delineation of porosity. Third, to incorporate geological insights into machine learning, we impose geologic constraints (GC) by measuring the data distribution similarity between around-the-well predictions and well labels. Compared with data augmentation strategies, incorporating spatial constraints and geologic constraints to STS yields more substantial improvements, which illustrates the importance of prior knowledge for physical parameter inversion. Finally, the combined application of these three strategies and the 3D STS method gives better generalization performance and geological plausibility in the porosity prediction for investigated carbonate reservoirs, outperforming other methods, and decreasing error by an average of 8% across 48 wells compared to STP.

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

Cited by 3 publications

References 57 publications

Research on fault data enhancement method of smart grid based on generation-countermine neural network

Research on fault data enhancement method of smart grid based on generation-countermine neural network

Porosity prediction from prestack seismic data via deep learning: incorporating a low-frequency porosity model

Enhancing seismic porosity estimation through 3D sequence-to-sequence deep learning with data augmentation, spatial constraints, and geologic constraints

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