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Parameter-shared variational auto-encoding adversarial network for desert seismic data denoising in Northwest China

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0 Published in 2021 at "Journal of Applied Geophysics"

DOI: 10.1016/j.jappgeo.2021.104428

Abstract: Abstract One of the key and difficult points in seismic data processing is seismic data denoising. Under the influence of the acquisition environment, the collected seismic data usually have low signal-to-noise ratio (SNR) and low… read more here.

Keywords: seismic data; data denoising; desert seismic; noise ... See more keywords

The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising

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1 Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2021.3073419

Abstract: For imaging and interpretation, high-quality seismic data are necessary. However, noise, which is strong in field desert seismic data, inevitably diminishes the quality of the data and reduces the signal-to-noise ratio. Moreover, the effective signals… read more here.

Keywords: seismic data; attentional generative; generative adversarial; application semisupervised ... See more keywords

Seismic Data Denoising With Correlation Feature Optimization Via S-Mean

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1 Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2021.3117965

Abstract: Random noise elimination acts as an important role in the seismic data processing. Moreover, protecting and recovering useful subsurface structure information are also significant. In this study, the S-mean that can obtain the geometric mean… read more here.

Keywords: denoising correlation; correlation; seismic data; data denoising ... See more keywords

Unsupervised CNN Based on Self-Similarity for Seismic Data Denoising

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1 Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2021.3131046

Abstract: Convolutional neural network (CNN)-based methods are powerful tools for seismic data denoising. Most methods adopt a supervised learning strategy, which requires noise-free labels to construct an objective function to guide the training of network parameters;… read more here.

Keywords: network; self similarity; data denoising; method ... See more keywords

Multigranularity Feature Fusion Convolutional Neural Network for Seismic Data Denoising

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1 Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2021.3123509

Abstract: Seismic data denoising is an important part of seismic data processing and has attracted much attention in recent years. With the rapid development of neural networks, convolutional neural network (CNN)-based denoising methods have been widely… read more here.

Keywords: data denoising; seismic data; feature fusion; convolutional neural ... See more keywords

An Intelligent Signal Processing Data Denoising Method for Control Systems Protection in the Industrial Internet of Things

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1 Published in 2021 at "IEEE Transactions on Industrial Informatics"

DOI: 10.1109/tii.2021.3096970

Abstract: The development of the IIOT paradigm brings the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent… read more here.

Keywords: intelligent signal; control systems; control; signal processing ... See more keywords

Data Denoising Based on Hadamard Matrix Transformation and Rayleigh Quotient Maximization: Application to GNSS Signal Classification

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1 Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2022.3184357

Abstract: Global navigation satellite system (GNSS) signal type classification based on machine learning is an effective way to improve urban positioning performance. However, GNSS signal type features extracted are unrelated, and the number of features is… read more here.

Keywords: matrix transformation; classification; rayleigh quotient; hadamard matrix ... See more keywords

Dictionary learning with convolutional structure for seismic data denoising and interpolation

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0 Published in 2021 at "Geophysics"

DOI: 10.1190/geo2019-0689.1

Abstract: Seismic data inevitably suffer from random noise and missing traces in field acquisition. This limits the use of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic… read more here.

Keywords: seismic data; dictionary learning; data denoising; denoising interpolation ... See more keywords