Seismic Impedance Inversion Using Conditional Generative Adversarial Network

Meng, Delin; Wu, Bangyu; Wang, Zhiguo; Zhu, Zhaolin

doi:10.1109/lgrs.2021.3090108

Cited by 25 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its original size is 600 × 501 × 502 (timeline, inline, crossline), for the convenience of training and inference, we resize it to 400 × 501 × 502 (timeline, inline, crossline). This data was denoised, which are shown in For SEAM Phase I, current methods often use 30 or more logs [11], [12], while we have reduced this number to 4 or 9 and achieved better performance. Our experiments were performed on 4 and 9 logs, and Fig.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Dou¹,

Li²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

Recent studies have shown that learning theories have been very successful in hydrocarbon exploration. Inversion of seismic into various attributes through the relationship of 1D well-logs and 3D seismic is an essential step in reservoir description, among which, acoustic impedance is one of the most critical attributes, and although current deep learningbased impedance inversion obtains promising results, it relies on a large number of logs (1D labels, typically more than 30 well-logs are required per inversion), which is unacceptable in many practical explorations. In this work, we define acoustic impedance inversion as a regression task for learning sparse 1D labels from 3D volume data and propose a voxel-wise semisupervised contrastive learning framework, ContrasInver, for regression tasks under sparse labels. ConstraInver consists of several key components, including a novel pre-training method for 3D seismic data inversion, a contrastive semi-supervised strategy for diffusing well-log information to the global, and a continuous-value vectorized characterization method for a contrastive learning-based regression task, and also designed the distance TopK sampling method for improving the training efficiency. We performed a complete ablation study on SEAM Phase I synthetic data to verify the effectiveness of each component and compared our approach with the current mainstream methods on this data, and our approach demonstrated very significant advantages. In this data we achieved an SSIM of 0.92 and an MSE of 0.079 with only four well-logs. ConstraInver is the first purely data-driven approach to invert two classic field data, F3 Netherlands (only four well-logs) and Delft (only three well-logs) and achieves very reasonable and reliable results.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To make the size of each sample in the batch consistent, we used TopK sampling and recorded the coordinates of the minimum K values from A dist in X aug 1 and X aug 2 , respectively. K, τ and ω must satisfy the equation (12),…”

Section: ) Log Information Diffusionmentioning

confidence: 99%

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Dou¹,

Li²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…(Wang et al, 2020) put forward a closed-loop CNN impedance inversion method to extract feature information from both labeled and unlabeled data, which alleviates the dependence of CNN on the amount of labeled data. (Meng et al, 2021) proposed a semi-supervised deep learning method based on Generative Adversarial Network (GAN), and the inversion results outperformed the traditional deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Low-frequency constrained seismic impedance inversion combining large kernel attention and long short-term memory

Wei,

Li,

Ning

et al. 2024

Acta Geophys.

View full text Add to dashboard Cite

In the seismic impedance inversion, the low-frequency information reflects the general trend of the impedance curve. Without low-frequency information, inversion results can not accurately reflect stratigraphic changes. Seismic data are also spatially correlated, while the conventional inversion methods do not consider the spatial correlation of geological structures, which may lead to poor lateral continuity of the inversion results. To alleviate these problems, we proposes a low-frequency constrained seismic impedance inversion method combining Large Kernel Attention (LKA) and Long Short-Term Memory (LSTM). Our network structure is divided into an inversion module and a low-frequency feature extraction module. In the inversion module, we integrate LKA and LSTM into the network, which can improve the lateral continuity of the inversion results. The low-frequency feature extraction module constrains the entire network structure and extract more refined low-frequency features. To demonstrate the reliability of the proposed method, we applied it to the SEAM model. Experiments show that our method has the best lateral continuity and accuracy, with Mean Squared Error (MSE) and Coefficient of Determination (R 2 ) of 0.0485 and 0.9164, respectively, as well as strong noise immunity. This method also achieves favorable inversion results on the Volve field seismic data.

show abstract

“…Dealing with big data with a huge number of parameters, DL has developed into a promising method to cope with different types of geophysical problems [5][6][7][8][9][10]. In recent years, many DL networks have been proposed for seismic impedance inversion, such as convolutional neural network (CNN) [11], fully convolutional residual network (FCRN) [12], constitutional neural network [13], recurrent neural network [14][15], generative adversarial networks [16][17][18] and so on. All of these DL methods achieve outstanding results.…”

Section: Introductionmentioning

confidence: 99%

“…Post-stack seismic data and impedance, serving as inputs and labels for data-driven methods, are corresponding trace by trace. Since a sequence of seismic data corresponds to an impedance sequence and there is a specific functional relationship between them, most DL methods train a model with one-dimensional (1D) convolution [12,[16][17]. To succeed in DL, it is important to provide more training examples than free parameters in deep networks with huge parameter space [20].…”

Section: Introductionmentioning

confidence: 99%

Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion

Xie

Ye³

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1D) convolution, tending to yield lateral discontinuities of impedance on field data applications. To alleviate this problem, we design a network equipped with two-dimensional (2D) convolutions and a coordinate attention (CA) block. The former can take the relationship between adjacent traces into consideration. The latter can capture the positional relationship of the geological structure, both horizontally and vertically. At the same time, we use a hybrid loss combined with edge operator and Mean Square Error (MSE) to further improve the stability of the designed network. Comparison experiments on synthetic SEAM model and field seismic data demonstrate the effectiveness of the adopted components, 2D convolution, CA and hybrid loss function, in improving the lateral continuity of inverted impedance. For field seismic data, the impedance predicted by proposed method shows improved lateral continuity and high resolution compared with the 1D network and Constrained Sparse Spike Inversion (CSSI) method using commercial software (InverTrace Plus module in Jason).

show abstract

Seismic Impedance Inversion Using Conditional Generative Adversarial Network

Cited by 25 publications

References 21 publications

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion

Low-frequency constrained seismic impedance inversion combining large kernel attention and long short-term memory

Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion

Contact Info

Product

Resources

About