Deep-learning-based seismic data interpolation: A preliminary result

Wang, Benfeng; Zhang, Ning; Lu, Wenkai; Wang, Jialin

doi:10.1190/geo2017-0495.1

Cited by 295 publications

(73 citation statements)

References 40 publications

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“…Within the area of seismic data interpolation and reconstruction, Wang et al . (,b) introduced an eight‐layer residual learning network (ResNet) based on CNNs to interpolate seismic data without aliasing. Mandelli et al .…”

Section: Introductionmentioning

confidence: 99%

Attenuation of marine seismic interference noise employing a customized U‐Net

Sun

Slang

Elboth

et al. 2020

Geophysical Prospecting

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A B S T R A C TMarine seismic interference noise occurs when energy from nearby marine seismic source vessels is recorded during a seismic survey. Such noise tends to be well preserved over large distances and causes coherent artefacts in the recorded data. Over the years, the industry has developed various denoising techniques for seismic interference removal, but although well performing, they are still time-consuming in use. Machine-learning-based processing represents an alternative approach, which may significantly improve the computational efficiency. In the case of conventional images, autoencoders are frequently employed for denoising purposes. However, due to the special characteristics of seismic data as well as the noise, autoencoders failed in the case of marine seismic interference noise. We, therefore, propose the use of a customized U-Net design with element-wise summation as part of the skip-connection blocks to handle the vanishing gradient problem and to ensure information fusion between high-and low-level features. To secure a realistic study, only seismic field data were employed, including 25,000 training examples. The customized U-Net was found to perform well, leaving only minor residuals, except for the case when seismic interference noise comes from the side. We further demonstrate that such noise can be treated by slightly increasing the depth of our network. Although our customized U-Net does not outperform a standard commercial algorithm in quality, it can (after proper training) read and process one single shot gather in approximately 0.02 s. This is significantly faster than any existing industry denoising algorithm. In addition, the proposed network processes shot gathers in a sequential order, which is an advantage compared with industry algorithms that typically require a multi-shot input to break the coherency of the noise.

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Section: Introductionmentioning

confidence: 99%

Attenuation of marine seismic interference noise employing a customized U‐Net

Sun

Slang

Elboth

et al. 2020

Geophysical Prospecting

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“…Yang [28] adapted the DL method to velocity estimation without requiring any prior information. Wang [29] introduced the DL strategy into seismic data interpolation to provide accurately reconstructed dense data. Hu [30] proposed the identification of the first arrivals by using convolution networks.…”

Section: Introductionmentioning

confidence: 99%

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

et al. 2020

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The emerging applications of deep learning in solving geophysical problems have attracted increasing attention. In particular, it is of significance to enhance the computational efficiency of the computationally intensive geophysical algorithms. In this paper, we accelerate deabsorption prestack time migration (QPSTM), which can yield higher-resolution seismic imaging by compensating absorption and correcting dispersion through deep learning. This is implemented by training a neural network with pairs of small-sized patches of the stacked migrated results obtained by conventional PSTM and deabsorption QPSTM and then yielding the high-resolution imaging volume by prediction with the migrated results of conventional PSTM. We use an encoder-decoder network to highlight the features related to high-resolution migrated results in a high-order dimension space. The training data set of small-sized patches not only reduces the required high-resolution migrated result (for instance, only several inline is required) but leads to a fast convergence in training. The proposed deep-learning approach accelerates the high-resolution imaging by more than 100 times. Field data is used to demonstrate the effectiveness of the proposed method.

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“…Based on a variety of principles and assumptions, important advances have been made in seismic data reconstruction. Some of them addressed interpolating regularly sampled data [13][14][15]20 , while some of them attacked non-uniformly sampled interpolation 21 . There are techniques developed for both irregularly and regularly missing data reconstruction 22 .…”

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confidence: 99%

“…Jia et al 14 proposed an intelligent interpolation method for regularly sampled data by Monte Carlo ML. Wang et al 15 proposed a DL-based approach for regularly sampled seismic data antialiasing interpolation. Based on CNNs, Wang et al 15 designed eight-layer residual networks (ResNets) with a better back-propagation property for interpolation, which extract feature maps of the training data in a non-linear way.…”

mentioning

confidence: 99%

“…Wang et al 15 proposed a DL-based approach for regularly sampled seismic data antialiasing interpolation. Based on CNNs, Wang et al 15 designed eight-layer residual networks (ResNets) with a better back-propagation property for interpolation, which extract feature maps of the training data in a non-linear way. For the methods of Jia and Ma 13 , Jia et al 14 , and Wang et al 15 , in the training process, to generate the input of the designed network, calculation of the initial pre-interpolation data using a bicubic method is required, which affects the final performance of their methods.…”

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confidence: 99%

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Deep learning for irregularly and regularly missing data reconstruction

Chai

Feng

et al. 2020

Sci Rep

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Deep learning (DL) is a powerful tool for mining features from data, which can theoretically avoid assumptions (e.g., linear events) constraining conventional interpolation methods. Motivated by this and inspired by image-to-image translation, we applied DL to irregularly and regularly missing data reconstruction with the aim of transforming incomplete data into corresponding complete data. to accomplish this, we established a model architecture with randomly sampled data as input and corresponding complete data as output, which was based on an encoder-decoder-style U-net convolutional neural network. We carefully prepared the training data using synthetic and field seismic data. We used a mean-squared-error loss function and an Adam optimizer to train the network. We displayed the feature maps for a randomly sampled data set going through the trained model with the aim of explaining how the missing data are reconstructed. We benchmarked the method on several typical datasets for irregularly missing data reconstruction, which achieved better performances compared with a peer-reviewed Fourier transform interpolation method, verifying the effectiveness, superiority, and generalization capability of our approach. Because regularly missing is a special case of irregularly missing, we successfully applied the model to regularly missing data reconstruction, although it was trained with irregularly sampled data only. Deep learning (DL) 1 is a branch of machine learning (ML) that addresses the question of how to build computers that intelligently improve through experience 2. Recently, DL or ML, in general, enjoyed an explosive growth and showed great promise in various areas, e.g., biology 3,4 , image reconstruction 5,6 , and solid earth geoscience 7. DL is powerful for mining features or relationships from data, which is invaluable in the context of big data, as it extracts high-level information from huge volumes of data. Please refer to Goodfellow et al. 8 for a good textbook of DL. One of the most popular DL technologies is the convolutional neural network (CNN), which is at the core of most state-of-the-art DL solutions for numerous tasks 9. In recent years, deep CNNs have had stunning successes, surpassing human accuracy for hard problems such as visual recognition 6. In exploration seismology, DL or ML has been widely used in fault detection 10 , structural interpretation 11 , inversion 12 , and data interpolation 13-15 , to name a few. A more tremendous trend of developments has recently come about through the use of DL not for image analysis but for image transformation. In these cases, CNNs are trained to transform one type of image into another. Many geophysical problems can be posed as transforming an input profile into a corresponding output profile (e.g., denoising: transforming noisy data to noise-free data). Inspired by Isola et al. 16 , where DL is investigated as a general-purpose solution to image-to-image translation problems, we apply DL to missing data reconstruction with the aim of transforming an...

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Deep-learning-based seismic data interpolation: A preliminary result

Cited by 295 publications

References 40 publications

Attenuation of marine seismic interference noise employing a customized U‐Net

Attenuation of marine seismic interference noise employing a customized U‐Net

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

Deep learning for irregularly and regularly missing data reconstruction

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