Multi-Attribute Classification Based On Sparse Autoencoder - A Gas Chimney Detection Example

Xu, Pengfei; Lu, Wei-Liang; Wang, B.F.

doi:10.3997/2214-4609.201700920

Cited by 4 publications

(1 citation statement)

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“…For example, Xiong et al [41] applied adaptive boosting (AdaBoost) to the design of the optimal learning algorithm for identifying gas chimneys, which generated more reliable results than the k-nearest neighbor method. Xu et al [42] implemented the sparse autoencoder for gas chimney detection, and the accuracy is greatly improved compared to the traditional MLP algorithm. The sparsity of the spatial distribution of gas chimneys in a seismic dataset adds the difficulty of reliable gas-chimney detection in two ways.…”

Section: E Gas-chimney Detectionmentioning

confidence: 99%

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

AlRegib

Deriche

Long

et al. 2018

IEEE Signal Process. Mag.

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Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms.Keywords subsurface imaging and exploration, seismic interpretation, digital signal processing, machine learning, human visual system, visual analytics

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Section: E Gas-chimney Detectionmentioning

confidence: 99%

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

AlRegib

Deriche

Long

et al. 2018

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

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A semi-supervised learning framework for gas chimney detection based on sparse autoencoder and TSVM

Wang

2019

Journal of Geophysics and Engineering

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

et al. 2019

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Seismic data interpolation is a longstanding issue. Most current methods are only suitable for randomly missing cases. To deal with regularly missing cases, an antialiasing strategy should be included. However, seismic survey design using a random distribution of shots and receivers is always operationally challenging and impractical. We have used deep-learning-based approaches for seismic data antialiasing interpolation, which could extract deeper features of the training data in a nonlinear way by self-learning. It can also avoid linear events, sparsity, and low-rank assumptions of the traditional interpolation methods. Based on convolutional neural networks, eight-layers residual learning networks (ResNets) with a better back-propagation property for deep layers is designed for interpolation. Detailed training analysis is also performed. A set of simulated data is used to train the designed ResNets. The performance is assessed with several synthetic and field data. Numerical examples indicate that the trained ResNets can help to reconstruct regularly missing traces with high accuracy. The interpolated results in the time-space domain and the frequency-wavenumber ([Formula: see text]-[Formula: see text]) domain demonstrate the validity of the trained ResNets. Even though the accuracy decreases with the increase of the feature difference between the test and training data, the proposed method can still provide reasonable interpolation results. Finally, the trained ResNets is used to reconstruct dense data with halved trace intervals for synthetic and field data. The reconstructed dense data are more continuous along the spatial direction, and the spatial aliasing effects disappear in the [Formula: see text]-[Formula: see text] domain. The reconstructed dense data have the potential to improve the accuracy of subsequent seismic data processing and inversion.

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Multi-Attribute Classification Based On Sparse Autoencoder - A Gas Chimney Detection Example

Cited by 4 publications

References 0 publications

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

A semi-supervised learning framework for gas chimney detection based on sparse autoencoder and TSVM

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

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