Results 71 to 80 of about 612 (172)

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

Towards a physics-informed network paradigm with data generation and background noise removal for different distributed acoustic sensing applications. [PDF]

Light Sci Appl
Wan Y, Wang H, Yu X, Chen J, Fan X, He Z.   +5 more
europepmc   +1 more source

Model-Driven Processing of Passive Seismic While Drilling Data Acquired Using Distributed Acoustic Sensing Without Conventional Drill-Bit Pilot Measurements. [PDF]

Sensors (Basel)
Al-Hemyari E, Pevzner R, Tertyshnikov K.
europepmc   +1 more source

Seismic random noise separation and suppression based on improved variational mode decomposition via grey wolf optimization. [PDF]

PLoS One
Yao Z, Li W, Zhu J, Hao L, Xing M.
europepmc   +1 more source

Improved first arrival picking of microseismic P-waves in coal mines using multi-denoising and adaptive characteristic functions. [PDF]

Sci Rep
Zhang Y, Zhan K, Song P, Liang C, Xu R, Kong C.   +5 more
europepmc   +1 more source

Hybrid multi-resolution network for DAS data denoising. [PDF]

PLoS One
Ding L, Sun H, Chen H, Hu X.
europepmc   +1 more source