Seismic data interpolation using deep learning with generative adversarial networks

Kaur, Harpreet; Pham, Nam

doi:10.1111/1365-2478.13055

Cited by 69 publications

(16 citation statements)

References 49 publications

(109 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The powerful learning ability of GAN is not only applicable to image generation. In recent years, GANs have continuously made breakthroughs in the fields of image synthesis, textto-image translation, style transfer, superresolution, and text generation in natural language processing [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Icon Art Design with Generative Adversarial Network under Deep Learning

Meng

Yang

Wang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the rapid development of the Internet, application interface design has undergone rapid changes. Numerous new design styles and resources have appeared; thus, a large number of interface icon design needs have been generated. Icons are quite different from ordinary photographed images, because they are all drawn by designers and have certain schematic and artistic features. Moreover, artistic icons can convey their drawn characteristics and meanings faster and better than captured images. The ideation process in icon design is time-consuming, and its design style and method of drawing are influenced by the device and the environment in which it is used. To simplify the process of icon design and enrich the creativity of icon conception, this study proposes to use the generative adversarial network technology in deep learning to train computers to generate artistic icons. This paper completes the construction of the icon generation model with generative adversarial network (GAN) model combined with the actual icon design process. For the problem of automatic icon generation, this paper does the following research work: (1) based on the conditional classification generative adversarial network, a multifeature icon generation model (MFIGM) is proposed. In the discriminator, a multifeature identification module is added to optimize the structure of the conditional feature to ensure that the icon generated by the model meets the given conditional feature. (2) Experiments on the icon dataset show that the MFIGM-based icon generation model proposed in this paper has better performance in designing various feature expressions of icons.

show abstract

Section: Introductionmentioning

confidence: 99%

Icon Art Design with Generative Adversarial Network under Deep Learning

Meng

Yang

Wang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Some recent studies have explored dictionary learning-based methods (Yu et al, 2016;Nazari Siahsar et al, 2017;Wang et al, 2020), which are considered more adaptive than fixed-basis transforms. Additionally, data reconstruction using machine learning techniques has been achieving encouraging results (Jia and Ma, 2017;Wang et al, 2019;Kaur et al, 2021).…”

Section: And Imagingmentioning

confidence: 99%

Low‐rank seismic data reconstruction and denoising by CUR matrix decompositions

Cavalcante

Porsani

2021

Geophysical Prospecting

View full text Add to dashboard Cite

Low-rank reconstruction methods assume that noiseless and complete seismic data can be represented as low-rank matrices or tensors. Therefore, denoising and recovery of missing traces require a reduced-rank approximation of the data matrix/tensor. To calculate such approximation, we explore the CUR matrix decompositions, which use actual columns and rows of the data matrix, instead of the costly singular vectors derived from singular value decomposition. By allowing oversampling columns and rows, CUR decompositions obviate the need for the exact rank. We evaluate three different procedures for randomly selecting columns and rows to obtain the CUR. Once the low-rank approximation is estimated, data reconstruction is achieved by an iterative optimization scheme. To demonstrate the effectiveness of CUR matrix decompositions for multidimensional seismic data recovery, we present examples of 3D and 4D synthetic and field data. Results derived by CUR compare well to conventional eigenimage-family methods.

show abstract

“…Kaur et al. (2021) proposed a DL approach based on generative adversarial networks (GANs; Goodfellow et al., 2014), which used two generators and one discriminator and satisfactorily eliminated spatial aliasing. Owing to their typical DNN design for obtaining seismic data from several shots, these networks are only suitable for exploratory data.…”

Section: Introductionmentioning

confidence: 99%

EWR‐Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet

et al. 2022

View full text Add to dashboard Cite

Owing to the limitations of surface conditions, the distribution of earthquake station arrays, even dense arrays, is uneven and spatially irregular. The station intervals are too large with respect to migration algorithm requirements. Therefore, the regularization of irregular station data is an important preprocessing step before imaging. Several methods, such as the curvelet transform method based on the sparse transform, have been developed for regularizing teleseismic data. In this study, we present a novel deep learning (DL) approach for teleseismic waveform regularization in 2D surveys. We designed an earthquake waveform regularization network (EWR‐Net) based on a deep generative model and residual network, consisting of a transposed convolution block, convolution block, and full connection block. The convolution block was variable and adjusted according to different data complexities to improve adaptability. The network was able to capture complex mapping between station locations and waveforms, and could be used to regularize both randomly and regularly sampled data without spatial smoothing. Unlike other DL methods, the EWR‐Net was trained and used for each event. It was trained using recorded teleseismic waveforms at irregular stations, and was then used to predict waveforms at regular stations. To avoid overfitting, L2 regularization, dropout in the full connection block, and early stopping were employed. The test results on both synthetic and field data showed that EWR‐Net could generate more accurate earthquake waveforms at virtual stations than the curvelet method. Reverse‐time migration imaging tests using regularized data demonstrated the feasibility of the proposed method.

show abstract

Seismic data interpolation using deep learning with generative adversarial networks

Cited by 69 publications

References 49 publications

Icon Art Design with Generative Adversarial Network under Deep Learning

Icon Art Design with Generative Adversarial Network under Deep Learning

Low‐rank seismic data reconstruction and denoising by CUR matrix decompositions

EWR‐Net: Earthquake Waveform Regularization Network for Irregular Station Data Based on Deep Generative Model and ResNet

Contact Info

Product

Resources

About