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Suppression of seismic random noise by deep learning combined with stationary wavelet packet transform
Many traditional denoising methods, such as Gaussian filtering, tend to blur and lose details or edge information while reducing noise. The...
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A diffusion model-based framework USDDM for random noise elimination of seismic signal
Seismic data acquired in seismic exploration is often contaminated by random noise, and it is necessary to develop effective seismic data denoising...
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Attention mechanism-based deep denoiser for desert seismic random noise suppression
Seismic data collected from desert areas contain a large amount of low-frequency random noise with similar waveforms to the effective signals. The...
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Adaptive time-reassigned synchrosqueezing transform for seismic random noise suppression
Noise suppression is of great importance to seismic data analysis, processing and interpretation. Random noise always overlaps seismic reflections...
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SeisGAN: Improving Seismic Image Resolution and Reducing Random Noise Using a Generative Adversarial Network
Seismic images are essential for understanding the subsurface geological structure and resource distribution. However, the accuracy and certainty of...
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Random noise attenuation in seismic data using an adaptive thresholding and the second-order variant time-reassigned synchrosqueezing transform
Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation...
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Elastic-Wave Reverse Time Migration Random Boundary-Noise Suppression Based on CycleGAN
In elastic-wave reverse-time migration (ERTM), the reverse-time reconstruction of source wavefield takes advantage of the computing power of GPU,...
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Random Noise Attenuation by Self-supervised Learning from Single Seismic Data
Random noise attenuation is of great importance to obtain high-quality seismic data. Unsupervised deep learning methods have received much attention...
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Random noise suppression of seismic data through multi-scale residual dense network
Random noise suppression is an important technique to improve the efficiency and accuracy of seismic data processing. Physical denoising methods such...
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Adaptive Damped Rank-Reduction Method for Random Noise Attenuation of Three-Dimensional Seismic Data
Rank-reduction methods are effective for separating random noise from the useful seismic signal based on the truncated singular value decomposition...
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Investigation of random noise in SYM-H and Dst during intense geomagnetic storms and solar quiet days of SC 23 using the method of potential analysis
AbstractPotential analysis (PA) method is used to investigate the randomness or stochastic noise in 1-minute Dst and SYM-H data during highly...
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A seismic random noise suppression method based on self-supervised deep learning and transfer learning
Random noise suppression is an essential task in the seismic data processing. In recent years deep learning methods have achieved superior results in...
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Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM)
AbstractAccurate modeling of tropospheric delays is crucial for the global navigation satellite system (GNSS), which finds extensive applications in...
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Random noise suppression and super-resolution reconstruction algorithm of seismic profile based on GAN
In this paper, we propose a random noise suppression and super-resolution reconstruction algorithm for seismic profiles based on Generative...
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Random Noise Attenuation in Tunnel Based on EMD-T-FSS
The non-stationary and non-continuous noises in the tunnel seismic data can cause huge noise spectrum estimation errors in time-frequency domain...
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Application of residual learning to microseismic random noise attenuation
Microseismic data which are recorded by near-surface sensors are usually drawn in strong random noise. The reliability and accuracy of arrivals...
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Probability Density Analysis of Nonlinear Random Ship Rolling
Ship rolling in random waves is a complicated nonlinear motion that contributes substantially to ship instability and capsizing. The finite element...
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Self-similarity convolution neural network for seismic noise suppression in desert environment
Seismic signals are inevitably disturbed by random noise in the acquisition process, which greatly degrades seismic data. In order to improve the...
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Research on the generation method of seawater sound velocity model based on Perlin noise
In the processing of conventional marine seismic data, seawater is often assumed to have a constant velocity model. However, due to static pressure,...
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Seismic noise attenuation using post-stack processing: a case study of Rabeh East Oil Field, Gulf of Suez Basin, Egypt
Seismic data are usually contaminated with random and coherent noise. This noise prevents the accurate imaging of seismic sections and lead to...
