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The EENSANE (Eastern European Seismic Ambient Noise) project: providing a new free database of ambient noise cross-correlations and crustal seismic models in the Carpathian-Pannonian Region and beyond
Ambient seismic noise has proven to be a particularly effective tool for subsurface imaging in the last decades, with applications ranging from near...
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Seismic noise between 0.003 Hz and 1.0 Hz and its classification
It is now established that the primary microseism, the secondary microseisms, and the hum are the three main components of seismic noise in 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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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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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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Three-Dimensional Urban Subsurface Space Tomography with Dense Ambient Noise Seismic Array
Two-dimensional dense seismic ambient noise array techniques have been widely used to image and monitor subsurface structure characterization in...
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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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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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Characteristics of secondary microseisms generated in the Bohai Sea and their impact on seismic noise
In this study, we use the Bohai Sea area as an example to investigate the characteristics of secondary microseisms and their impact on seismic noise...
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The effect of 2020 Covid-19 pandemic lockdowns on seismic ambient noise recorded in Eastern Dharwar region, south-eastern India
In March 2020, after the declaration of the COVID-19 pandemic by the World Health Organization (WHO), all the countries worldwide imposed a series of...
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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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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...
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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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Evaluation of the Hyderabad network’s broadband seismic stations for characterizing ambient noise in the Eastern Dharwar Craton, southern India
AbstractThe ambient noise analysis was performed at ten installed broadband seismic stations of the Eastern Dharwar Craton in Telangana state, India....
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Solar Flares, Strong Magnetic Storms, and Variations in the Level of Seismic Noise in the Northern Tien Shan
AbstractThe relationship between strong magnetic storms caused by X-class solar flares in solar cycles 23 and 24 and seismicity variations (seismic...
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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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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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Filling the gap of seismic ambient noise taken from the earth by modification of the frequency content of the existing time series
Seismic ambient noise is increasingly important in estimating velocity and attenuation structures, as well as understanding parameter variations....
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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...
