Results 81 to 90 of about 4,173 (187)

Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

IEEE Access, 2019
Due to the limitation of the seismic data acquisition environment and instrument, seismic data are often subjected to random noise interference. At the same time, random noise is inevitably introduced in the processing of seismic data.
Wen-Long Hou, Rui-Sheng Jia, Hong-Mei Sun, Xing-Li Zhang, Meng-Di Deng, Yu Tian   +5 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

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

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
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

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

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

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