Results 71 to 80 of about 672 (170)

Enhanced Hardrock Seismic Imaging Through Multi‐Scale Information‐Guided Unsupervised Learning

Journal of Geophysical Research: Machine Learning and Computation
In hardrock or crystalline rock geological settings, due to low impedance contrast, reflected energy is usually weak. In addition, often stronger surface waves and noncoherent noise are observed including high‐frequency scattering noise, which seriously ...
Liuqing Yang, Alireza Malehmir, Magdalena Markovic   +2 more
doaj   +1 more source

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

Seismic Random Noise Attenuation Using DARE U-Net

Remote Sensing
Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces ...
Tara P. Banjade, Cong Zhou, Hui Chen, Hongxing Li, Juzhi Deng, Feng Zhou, Rajan Adhikari   +6 more
doaj   +1 more source

End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms

大数据
Denoising of seismic waveform signals is crucial for seismic monitoring and seismological research. To this end, we propose an end-to-end deep learning method for denoising seismic waveforms.
Zhao Botao, Kang Zuheng, He Yayun, Peng Junqing, Zhang Xulong, Qu Xiaoyang, Tan Yipei, Chen Yule, Xiao Chunguang, Wang Jianzong   +9 more
doaj  

MFIEN: multi-scale feature interactive enhancement network for seismic data denoising in desert areas. [PDF]

Sci Rep
Zhong T, Ye Y.
europepmc   +1 more source

Automated seismic fault mapping using convolutional neural networks for the Berenice Field, Western Desert, Egypt. [PDF]

Sci Rep
Amer M, Mabrouk WM, Eid AM, Metwally A.
europepmc   +1 more source

Fault detection in seismic data using a true 3D global attention convolutional network with self-supervised denoising pretext training. [PDF]

Sci Rep
Mahzad M, Bagheri M.
europepmc   +1 more source

Machine Learning and Deep Learning for Earthquake Monitoring: A Systematic Review of Distributed Acoustic Sensing Applications. [PDF]

Sensors (Basel)
Iqbal N, Aziz IBA, Hakimi HSB, Raza MF, Rashid A.   +4 more
europepmc   +1 more source

Imaging Earth's subsurface with thunderstorm-generated seismic waves. [PDF]

Sci Adv
Roth N, Kim D, Czarny R, Kim YC, Wauthier C, Zhu T.   +5 more
europepmc   +1 more source

Curvelet-Based Stochastic Noise Suppression for Downhole DAS Microseismic Data. [PDF]

Sensors (Basel)
Zhang Y, Chen Z, Chen H, Cheng L, Dong J, Wu Z, Li Z.   +6 more
europepmc   +1 more source