Results 141 to 150 of about 612 (172)
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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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Introduction to Denoising and Data Gap Filling of Seismic Reflection Data
2020Seismic data is a mixture of several wanted (reflections, refractions) and unwanted (ground roll, diffractions, airwave etc.) signals. Different wave fields recorded by the seismic receiver can be seen from Fig. 1.1. Separation of wanted signal from such unwanted noises is therefore fundamental in geophysical signal processing.
R. K. Tiwari, R. Rekapalli
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Application of improved AlexNet for seismic data denoising
International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 2023ji shangran +4 more
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Similarity-Informed Self-Learning and Its Application on Seismic Image Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2022Naihao Liu +2 more
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An adaptive seismic signal denoising method based on variational mode decomposition
Measurement: Journal of the International Measurement Confederation, 2021Qiuzhan Zhou, Pingping Liu
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