Signal extraction using randomized-order multichannel singular spectrum analysis

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V69-V84 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Yimin Yuan ◽  
Shuwei Gan ◽  
Yangkang Chen
Keyword(s):  
Spectrum Analysis ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Data Sets ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Singular Spectrum ◽  
3D Data ◽  
Time Space ◽  
Ground Roll

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.

Damped multichannel singular spectrum analysis for 3D random noise attenuation

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. V261-V270 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Yangkang Chen ◽  
Huijian Li ◽  
Shuwei Gan
Keyword(s):  
Spectrum Analysis ◽  
Seismic Data ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Superior Performance ◽  
Damping Factor ◽  
Signal Subspace ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Singular Spectrum

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation in seismic data, which decomposes the vector space of the Hankel matrix of the noisy signal into a signal subspace and a noise subspace by truncated singular value decomposition (TSVD). However, this signal subspace actually still contains residual noise. We have derived a new formula of low-rank reduction, which is more powerful in distinguishing between signal and noise compared with the traditional TSVD. By introducing a damping factor into traditional MSSA to dampen the singular values, we have developed a new algorithm for random noise attenuation. We have named our modified MSSA as damped MSSA. The denoising performance is controlled by the damping factor, and our approach reverts to the traditional MSSA approach when the damping factor is sufficiently large. Application of the damped MSSA algorithm on synthetic and field seismic data demonstrates superior performance compared with the conventional MSSA algorithm.

A Preliminary Investigation into the Effect of Outlier(s) on Singular Spectrum Analysis

2014 ◽  
Vol 13 (04) ◽  
pp. 1450029 ◽  
Author(s):  
Hossein Hassani ◽  
Rahim Mahmoudvand ◽  
Hardi Nabe Omer ◽  
Emmanuel Sirimal Silva
Keyword(s):  
Spectrum Analysis ◽  
Preliminary Investigation ◽  
Singular Spectrum Analysis ◽  
Real Data ◽  
Data Sets ◽  
Singular Spectrum ◽  
The Matrix ◽  
Points Of View ◽  
Different Parts

The aim of this paper is to study the effect of outliers on different parts of singular spectrum analysis (SSA) from both theoretical and practical points of view. The rank of the trajectory matrix, the magnitude of eigenvalues, reconstruction, and forecasting results are evaluated using simulated and real data sets. The performance of both recurrent and vector forecasting procedures are assessed in the presence of outliers. We find that the existence of outliers affect the rank of the matrix and increases the linear recurrent dimensions whilst also having a significant impact on SSA reconstruction and forecasting processes. There is also evidence to suggest that in the presence of outliers, the vector SSA forecasts are more robust in comparison to the recurrent SSA forecasts. These results indicate that the identification and removal of the outliers are mandatory to achieve optimal SSA decomposition and forecasting results.

Spectral structure-oriented filtering of seismic data with self-adaptive paths

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V271-V280
Author(s):  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Noise Removal ◽  
Quality Data ◽  
Spectral Structure ◽  
Data Sets ◽  
Coherent Noise ◽  
Edge Preserving ◽  
3D Data ◽  
Self Adaptive

We have developed an algorithm to perform structure-oriented filtering (SOF) in 3D seismic data by learning the data structure in the frequency domain. The method, called spectral SOF (SSOF), allows us to enhance the signal structures in the [Formula: see text]-[Formula: see text]-[Formula: see text] domain by running a 1D edge-preserving filter along curvilinear self-adaptive trajectories that connect points of similar characteristics. These self-adaptive paths are given by the eigenvectors of the smoothed structure tensor, which are easily computed using closed-form expressions. SSOF relies on a few parameters that are easily tuned and on simple 1D convolutions for tensor calculation and smoothing. It is able to process a 3D data volume with a 2D strategy using basic 1D edge-preserving filters. In contrast to other SOF techniques, such as anisotropic diffusion, anisotropic smoothing, and plane-wave prediction, SSOF does not require any iterative process to reach the denoised result. We determine the performance of SSOF using three public domain field data sets, which are subsets of the well-known Waipuku, Penobscot, and Teapot surveys. We use the Waipuku subset to indicate the signal preservation of the method in good-quality data when mostly background random noise is present. Then, we use the Penobscot subset to illustrate random noise and footprint signature attenuation, as well as to show how faults and fractures are improved. Finally, we analyze the Teapot stacked and depth-migrated subsets to show random and coherent noise removal, leading to an improvement of the volume structural details and overall lateral continuity. The results indicate that random noise, footprints, and other artifacts can be successfully suppressed, enhancing the delineation of geologic structures and seismic horizons and preserving the original signal bandwidth.

Seismic noise attenuation using an online subspace tracking algorithm

2020 ◽  
Vol 222 (3) ◽  
pp. 1765-1788
Author(s):  
Yatong Zhou ◽  
Shuhua Li ◽  
Dong Zhang ◽  
Yangkang Chen
Keyword(s):  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Computational Cost ◽  
Tracking Algorithm ◽  
Low Rank ◽  
Noise Attenuation ◽  
Subspace Tracking ◽  
Coherent Noise ◽  
Denoising Method ◽  
Residual Noise

SUMMARY We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

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