Results 31 to 40 of about 4,173 (187)

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Frontiers in Earth Science, 2023
In recent years, distributed optical fiber acoustic sensing (DAS) technology has been increasingly used for vertical seismic profile (VSP) exploration. Even though this technology has the advantages of high spatial resolution, strong resistance to high ...
Cong Wang, Xingguo Huang, Yue Li, Kristian Jensen   +3 more
doaj   +1 more source

Diffusion Model for DAS-VSP Data Denoising

Sensors, 2023
Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models,
Donglin Zhu, Lei Fu, Vladimir Kazei, Weichang Li   +3 more
doaj   +1 more source

Seismic data denoising and deblending using deep learning

CoRR, 2019
An important step of seismic data processing is removing noise, including interference due to simultaneous and blended sources, from the recorded data. Traditional methods are time-consuming to apply as they often require manual choosing of parameters to obtain good results.
Alan Richardson, Caelen Feller
openaire   +2 more sources

Seismic Fault Preserving Diffusion

, 2007
This paper focuses on the denoising and enhancing of 3-D reflection seismic data. We propose a pre-processing step based on a non linear diffusion filtering leading to a better detection of seismic faults. The non linear diffusion approaches are based on
Admasu, Bakker, Bakker, Berthoumieu, Bigün, Catte, Christian Germain, Dargent, Dargent, Dosil, Freeman, Gauthier, Gerig, Granlund, Koenderink, Marc Donias, Martens, Monteil, Naamen Keskes, Olivier Lavialle, Perona, Randen, Rao, Sebastien Guillon, Sorin Pop, Sønneland, Terebes, Terebes, Terebes, Van Ginkel, Van Kempen, Weickert, Weickert, Weickert, Weickert, Whitaker, Yannick Berthoumieu   +36 more
core   +4 more sources

Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising

Elektronika ir Elektrotechnika
Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and
Chao Liu, Ziang Wang, Yaping Huang, Aiping Zeng, Hongming Fan   +4 more
doaj   +1 more source

Ground-Truth-Free 3D Seismic Denoising Based on Diffusion Models: Achieving Effective Constraints Through Embedded Self-Supervised Noise Modeling

Remote Sensing
Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution.
Zhonghan Zhang, Guihe Qin, Yanhua Liang, Minghui Sun, Yingqing Wang, Jiaru Song   +5 more
doaj   +1 more source

Bolt Detection Signal Analysis Method Based on ICEEMD

, 2018
The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal ...
Guo, Chunhui, Xie, Xin, Yang, Zhengyu, Zhang, Zhan   +3 more
core   +2 more sources

A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

Applied Sciences, 2021
Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation.
Milan Brankovic, Eduardo Gildin, Richard L. Gibson, Mark E. Everett   +3 more
doaj   +1 more source

Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain

IEEE Access, 2020
Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much
Yu Sang, Jinguang Sun, Xiangfu Meng, Haibo Jin, Yanfei Peng, Xinjun Zhang   +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

Environmental Microbiology Reports, Volume 18, Issue 3, June 2026.
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.
Rajendra Prasad Sahu, Sufia Khannam Kazy, Debarshi Mukherjee, Sukanta Roy, Thomas Wiersberg, Pinaki Sar   +5 more
wiley   +1 more source