Results 121 to 130 of about 612 (172)

Q-Compensated Denoising of Seismic Data

IEEE Transactions on Geoscience and Remote Sensing, 2021
It is widely known that strong noise can decrease the quality of seismic data. However, the anelastic attenuation could be more important to account for the weak amplitude and low quality of seismic data. Here, we develop an inversion framework to simultaneously compensate for the attenuation of seismic data and remove noise, thereby enhancing the ...
Hang Wang, Guangtan Huang, Wei Chen 0031, Yangkang Chen   +3 more
openaire   +1 more source

DnResNeXt Network for Desert Seismic Data Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
In recent years, the denoising of low-frequency desert noise has been the significant and difficult point in processing seismic data. Traditional random noise suppression methods could not get a good result in processing seismic data in desert areas. Moreover, convolutional neural network (CNN) has made notable achievements in many fields recently.
Haiyang Yao, Haitao Ma 0001, Yue Li 0003, Qiankun Feng   +3 more
openaire   +1 more source

Self-Supervised Learning for Seismic Data Reconstruction and Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
With their powerful feature extraction ability, convolutional neural network (CNN) models achieve excellent signal reconstruction and recovery performances compared with those of traditional methods. The CNN-based approaches mainly use supervised learning approaches; thus, they require large numbers of ground-truth labeled samples.
Fanlei Meng, Qinyin Fan, Yue Li 0003
openaire   +1 more source

Seismic denoising diffusion restoration model for seismic data processing

Engineering Applications of Artificial Intelligence
Yimin Dou, Zhixuan Yang
exaly   +2 more sources

Seismic Data Denoising Based on Tensor Decomposition With Total Variation

IEEE Geoscience and Remote Sensing Letters, 2021
In order to remove random noise in seismic data, this letter proposes a seismic data denoising method based on tensor decomposition and total variation (TDTV). Based on the self-similarity of seismic data, this method first groups similar patches into a stack, then utilizes the low-rank tensor approximation strategy to restore the structural effective ...
Jun Feng, Xiaoqin Li
exaly   +2 more sources

Attribute-Based Double Constraint Denoising Network for Seismic Data

IEEE Transactions on Geoscience and Remote Sensing, 2021
At present, most of the seismic data denoising methods based on deep learning attempt to establish a synthetic seismic data set as the network training set to train network parameters. However, the synthetic data set cannot completely reflect the structural characteristics of the field seismic data, resulting in some false seismic reflections in field ...
Shengnan Wang, Yue Li, Ning Wu
exaly   +2 more sources

Unsupervised Seismic Data Denoising Using Diffusion Denoising Model

IEEE Transactions on Geoscience and Remote Sensing
Fuyao Sun, Hongbo Lin, Yue Li
exaly   +2 more sources

Curvelet Transform and its Application in Seismic Data Denoising

2009 International Conference on Information Technology and Computer Science, 2009
Curvelet transform is a new multi-scale transform developed upon wavelet transform. Beside scale and position, its constructive factors still include directions. All these make curvelet transform have a better directional characteristic. Based on these properties, we transform seismic data into curvelet domain, apply a window-shrinking algorithm to ...
Junhua Zhang, Xiaoteng Liang, Jinrong Fu, Jianle Guo, Xugang Zheng   +4 more
openaire   +1 more source

Adaptive Dictionary Learning for Blind Seismic Data Denoising

IEEE Geoscience and Remote Sensing Letters, 2020
The data-driven tight frame (DDTF) method is a dictionary learning method which has been used widely in the adaptive sparse representation and the seismic random noise attenuation. In the DDTF method, the thresholding operator setting plays a significant role on balancing the noise removal and preservation of detail information with high frequency. The
Xiaojing Wang, Jianwei Ma
openaire   +1 more source

Unsupervised CNN Based on Self-Similarity for Seismic Data Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
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; however, it is impossible to obtain true noise-free field data.
Wenqian Fang, Lihua Fu, Hongwei Li 0003
openaire   +1 more source