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Spatiotemporal Anomaly Detection in Distributed Acoustic Sensing Using a GraphDiffusion Model. [PDF]

Sensors (Basel)
Jeong S, Kim H, Kim YH, Park CS, Jung H, Kim HK.   +5 more
europepmc   +1 more source

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Generative Adversarial Network for Desert Seismic Data Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2021
Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation.
Hongzhou Wang, Yue Li, Xintong Dong
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

Seismic denoising diffusion restoration model for seismic data processing

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

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