Results 11 to 20 of about 612 (172)

Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis

Frontiers in Earth Science, 2023
The signal-to-noise ratio (SNR) of seismic data is the key to seismic data processing, and it also directly affects interpretation of seismic data results.
Guangde Zhang, Huaibang Zhang, Li You, Yuyong Yang, Huailai Zhou, Bohan Zhang, Wujin Chen, Liyuan Liu   +7 more
doaj   +1 more source

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Frontiers in Earth Science, 2023
Convolutional Neural Network (CNN) is widely used in seismic data denoising due to its simplicity and effectiveness. However, traditional seismic denoising methods based on CNN ignore multi-scale features of seismic data in the wavelet domain.
Liangsheng He, Liangsheng He, Hao Wu, Hao Wu, Xiaotao Wen, Xiaotao Wen   +5 more
doaj   +1 more source

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

Micromachines, 2022
In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on ...
Jiening Xia, Zhigao Chen, Jiang Yang, Yuxiu Chen, Benyan Tan, Zongxuan Wu   +5 more
doaj   +1 more source

Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN

Remote Sensing, 2022
The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of ...
Wenda Li, Tianqi Wu, Hong Liu
doaj   +1 more source

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

IEEE Access, 2022
Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process.
Liang Guo, Renze Luo, Xingyu Li, Yang Zhou, Tuo Juanjuan, Canru Lei   +5 more
doaj   +1 more source

Ecological Processes Underpinning Microbial Variability in Archean Granitoids Beneath the Deccan Traps: Evidence From Deep Drilling in Koyna, India. [PDF]

Environ Microbiol Rep
Deep biosphere hosted by Archean granitoid basement of Deccan Traps showed depth‐wise microbial partitioning. Limited dispersion and variable selection control community assembly. Fewer abundant bacterial taxa were ubiquitous, while large numbers of rare taxa remained localized.
Sahu RP, Kazy SK, Mukherjee D, Roy S, Wiersberg T, Sar P.   +5 more
europepmc   +2 more sources

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
In seismic data processing, denoising and reconstruction are the two steps for identification of resources in the earth subsurface layers. The seismic data quality is affected by random noise and interference during acquisition.
Lakshmi Kuruguntla, Vineela Chandra Dodda, Anup Kumar Mandpura, Sunil Chinnadurai, Karthikeyan Elumalai   +4 more
doaj   +1 more source

Seismic random noise suppression using improved CycleGAN

Frontiers in Earth Science, 2023
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun, Guihua Li, Renwei Ding, Lihong Zhao, Lihong Zhao, Yujie Zhang, Shuo Zhao, Jinwei Zhang, Junlin Ye   +8 more
doaj   +1 more source

A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

Remote Sensing, 2022
Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been
Haixia Zhao, Tingting Bai, Zhiqiang Wang
doaj   +1 more source

Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet

Energies, 2022
Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than
Hongfei Qian, Xiangchun Wang, Xuelei Chen, Zhu Yang   +3 more
doaj   +1 more source