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Application of Wavelet Analysis in Denoising Seismic Data
Applied Mechanics and Materials, 2014The random noise is the kind of noise with wide frequency band in seismic data detected by the optical acceleration sensors. The noises influence and destroy the useful signal of the seismic information. There are a lot of methods to remove noise and one of the standard methods to remove the noise of the signal was the fast Fourier transform (FFT ...
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Damped Dreamlet Representation for Exploration Seismic Data Interpolation and Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2018The dreamlet (drumbeat-beamlet) transform can provide us an efficient method to represent physical wavefield, because the dreamlet basis satisfies automatically the wave equation, which is a distinctive feature different from mathematical basis, such as Fourier and curvelet.
Weilin Huang, Ru-Shan Wu, Runqiu Wang
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Mask-Guided Model for Seismic Data Denoising
IEEE Geoscience and Remote Sensing Letters, 2022Ziyi Fang, Hongbo Lin, Xinyu Xu
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A Novel Convolutive ICA for Seismic Data Denoising
2012A main task of geophysical exploration is to remove random noises in seismic data processing to improve the SNR. Recently blind source separation (BSS) theory is applied to remove seismic random noises. But most are based on the instantaneous mixture model and limited to the synthetic seismic records.
Tian Yanan +4 more
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DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data
IEEE Transactions on Neural Networks and Learning Systems, 2023Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-noise ratio (SNR) of registered seismic signals improves subsequent processing and, eventually, data analysis and interpretation. In this work, a novel noise reduction framework based on an intelligent deep convolutional neural network is proposed that works on ...
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Local quantum filtering and denoising of seismic data
GeophysicsABSTRACT The precise characterization and analysis of complex geologic targets in seismic reflection data are posing increasingly high demands on the capabilities of signal processing methods. A recent contribution to signal processing is the Schrödinger equation-based adaptive transformation, which projects seismic data onto a ...
Ya-Juan Xue +5 more
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Curvelets - A Versatile Tool for Denoising Seismic Data
70th EAGE Conference and Exhibition incorporating SPE EUROPEC 2008, 2008This paper demonstrates that the curvelet transform is a simple yet powerful and flexible tool for denoising both stacked and prestack seismic data. The ability of curvelet-based denoising to provide superior noise attenuation with minimal impact on the desirable signal is illustrated using stacked data.
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Multiscale Spatial Attention Network for Seismic Data Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2022Xintong Dong +4 more
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Multi-Scale GAN with NSCT Prediction for Seismic Data Denoising
International Journal of Pattern Recognition and Artificial IntelligenceIn this paper, we focus on denoising seismic signals effectively to obtain high-quality data, which is crucially important for oil-gas reservoir prediction and seismic interpretation tasks. The rapid progress of deep learning has brought new development opportunities to seismic oil and gas exploration technologies. However, current deep learning-based
Cong Tang +5 more
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SAPD: Self-Attention Progressive Seismic Data Denoising
2023 International Conference on Machine Learning and Cybernetics (ICMLC), 2023Ke Wang 0049 +3 more
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