Results 91 to 100 of about 4,442 (191)

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Remote Sensing
In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss.
Mu Ding, Yatong Zhou, Yue Chi
doaj   +1 more source

Seismic random noise attenuation using modified wavelet thresholding

Annals of Geophysics, 2017
In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random
Qi-sheng Zhang, Jin-juan Jiang, Jin-hai Zhai, Xin-yue Zhang, Yi-jun Yuan, Xin-wu Huang   +5 more
doaj   +1 more source

Geophysical data denoising using dictionary learning method with Ramanujan sums for oil and minerals exploration

Artificial Intelligence in Geosciences
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio (SNR) and helps identify oil and minerals. Dictionary learning (DL) is a promising method for noise attenuation.
Lakshmi Kuruguntla, Mamatha Bandaru, Dokku Tejaswi, Anup Kumar Mandpura, Sravan Kumar Sikhakoli, Vineela Chandra Dodda   +5 more
doaj   +1 more source

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Applied Sciences
Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain.
Ming Wei, Xinlei Sun, Jianye Zong
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Collaborative denoising network for blind separation of seismic data: denoising of seismic signals under urban multi-source noise

Measurement Science and Technology
Abstract With the rapid development of technology and the growth of the global population, the development of above ground space is insufficient to meet the needs of modern society. Therefore, the coordinated development of above ground and underground spaces is crucial for future smart cities.
Yuqi Wang, Xintong Dong, Xunqian Tong, Hongyuan Yang, Linhang Zhang, Huaizhu Zhang   +5 more
openaire   +1 more source

Nonlinear Seismic Signal Denoising Using Template Matching with Time Difference Detection Method

Remote Sensing
As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in
Rongwei Xu, Bo Feng, Huazhong Wang, Chengliang Wu, Zhenbo Nie   +4 more
doaj   +1 more source

Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences

IEEE Access
Suppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the
Jian Gao, Zhenchun Li, Min Zhang, Wanyue Gao, Yixuan Gao   +4 more
doaj   +1 more source

Research on High-Density Discrete Seismic Signal Denoising Processing Method Based on the SFOA-VMD Algorithm

Geosciences
With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths
Xiaoji Wang, Kai Lin, Guangzhao Guo, Xiaotao Wen, Dan Chen   +4 more
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Evaluating Scalograms for Seismic Event Denoising

, 2021
Phillip Lewis, Antonio Gonzales, Patrick Hammond   +2 more
openaire   +2 more sources

Application of a Fractional Laplacian-Based Adaptive Progressive Denoising Method to Improve Ambient Noise Crosscorrelation Functions

Fractal and Fractional
Extracting high-quality surface wave dispersion curves from crosscorrelation functions (CCFs) of ambient noise data is critical for seismic velocity inversion and subsurface structure interpretation. However, the non-uniform spatial distribution of noise
Kunpeng Yu, Jidong Yang, Shanshan Zhang, Jianping Huang, Weiqi Wang, Tiantao Shan   +5 more
doaj   +1 more source