Is the sparsity enough? Random noise suppression in prestack seismic data using sparsity and lowrankness simultaneously

2020 ◽  
Author(s):  
Weiwei Xu* ◽  
Wenchao Chen ◽  
Yanhui Zhou ◽  
Xiaokai Wang ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Non-stationary predictive filtering for seismic random noise suppression - A tutorial

Geophysics ◽  
2021 ◽  
pp. 1-79 ◽  
Author(s):  
Hang Wang ◽  
Wei Chen ◽  
Weilin Huang ◽  
Shaohuan Zu ◽  
Xingye Liu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Space Dimension ◽  
Noise Suppression ◽  
Random Noise ◽  
Spatial Dimension ◽  
Seismic Data Processing ◽  
Filtering Method ◽  
Time Space ◽  
Stationary Version ◽  
Predictive Filtering

Predictive filtering in the frequency domain is one of the most widely used denoising algorithms in the seismic data processing workflow. Predictive filtering is based on the assumption of linear/planar events in the time-space domain. In traditional predictive filtering method, the predictive filter is fixed across the spatial dimension, which cannot deal with the spatial variation of seismic data well. To handle the curving events, the predictive filter is either applied in local windows or extended to a non-stationary version. The regularized non-stationary autoregression (RNAR) method can be treated as a non-stationary extension of the traditional predictive filtering, where the predictive filter coefficients are variable in different space locations. The highly under-determined inverse problem is solved by shaping regularization with a smoothness constraint in space. We further extend the RNAR method to a more general case, where we can apply more constraints to the filter coefficients according to the features of seismic data. First, apart from the smoothness in space, we also apply a smoothing constraint in frequency, considering the coherency of the coefficients in the frequency dimension. Secondly, we apply a frequency dependent smoothing radius along the space dimension to better take advantage of the non-stationarity of seismic data in the frequency axis, and to better deal with noise. The proposed method is validated via several synthetic and field data examples.

Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

2019 ◽  
Vol 219 (2) ◽  
pp. 1281-1299 ◽  
Author(s):  
X T Dong ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Order Statistic ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Low Frequency ◽  
High Order ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
High Order Statistic ◽  
Adaptive Noise

SUMMARY The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.

Improvement of S/N Ratio of Seismic Data by Multi-Channel Wiener Filter Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1501-1505
Author(s):  
Xue Hao ◽  
Na Li ◽  
Lin Ren
Keyword(s):  
Noise Reduction ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Wiener Filter

Noise reduction or cancellation is important for getting clear and useful signals. This paper deals with the implementation of the multi-channel wiener filter algorithm for noise suppression of seismic data. Known the velocity of reflection event, utilizes the resemblance of reflection signal in each seismic trace, the multi-channel wiener filter algorithm is effective in enhance reflection event and suppress the random noise. This algorithm is used to CDP gathers and the simulation shows the method is effective.

scholarly journals A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation

2022 ◽  
Vol 14 (2) ◽  
pp. 263
Author(s):  
Haixia Zhao ◽  
Tingting Bai ◽  
Zhiqiang Wang
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Field Data ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Natural Images ◽  
Training Data ◽  
Learning Approaches ◽  
Learning Method

Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.

A Random Noise Suppression Method of Seismic Data Based on the VMD-ICA Algorithm

2021 ◽  
Author(s):  
H. Meng ◽  
Q. Su ◽  
H. Zeng ◽  
X. Xu ◽  
H. Liu
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Suppression Method ◽  
Ica Algorithm

Random noise suppression for seismic data using a non-local Bayes algorithm

2018 ◽  
Vol 15 (1) ◽  
pp. 91-98 ◽  
Author(s):  
De-Kuan Chang ◽  
Wu-Yang Yang ◽  
Yi-Hui Wang ◽  
Qing Yang ◽  
Xin-Jian Wei ◽  
...  
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Non Local ◽  
Bayes Algorithm

scholarly journals Self-supervised learning for random noise suppression in seismic data

2021 ◽  
Author(s):  
Claire Birnie ◽  
Matteo Ravasi ◽  
Tariq Alkhalifah
Keyword(s):  
Supervised Learning ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise

scholarly journals The potential of self-supervised networks for random noise suppression in seismic data

2021 ◽  
Author(s):  
Claire Birnie ◽  
Matteo Ravasi ◽  
Sixiu Liu ◽  
Tariq Alkhalifah
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise
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