Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation (original) (raw)
Related papers
Compression denoising: using seismic compression for uncoherent noise removal
2001
Wavelet related techniques have been proved successful in many seismic processing applications, such as filtering or compression. While seismic data compression is not yet widely accepted, we propose a compression based on filter banks as a means to remove uncoherent noise from seismic data, and thus improve the SNR. Results are demonstrated on synthetic data.
5D seismic data completion and denoising using a novel class of tensor decompositions
GEOPHYSICS, 2015
We have developed a novel strategy for simultaneous interpolation and denoising of prestack seismic data. Most seismic surveys fail to cover all possible source-receiver combinations, leading to missing data especially in the midpoint-offset domain. This undersampling can complicate certain data processing steps such as amplitude-variation-with-offset analysis and migration. Data interpolation can mitigate the impact of missing traces. We considered the prestack data as a 5D multidimensional array or otherwise referred to as a 5D tensor. Using synthetic data sets, we first found that prestack data can be well approximated by a low-rank tensor under a recently proposed framework for tensor singular value decomposition (tSVD). Under this low-rank assumption, we proposed a complexity-penalized algorithm for the recovery of missing traces and data denoising. In this algorithm, the complexity regularization was controlled by tuning a single regularization parameter using a statistical te...
Using a novel method for random noise reduction of seismic records
2018
Random or incoherent noise is an important type of seismic noise, which can seriously affect the quality of the data. Therefore, decreasing the level of this category of noises is necessary for increasing the signal-to-noise ratio (SNR) of seismic records. Random noises and other events overlap each other in time domain, which makes it difficult to attenuate them from seismic records. In this research, a new technique is produced, by joining FX deconvolution (FXD) and a special kind of median filter in order to suppress random noise from seismic records. The technique is operated in some stages; firstly, FXD is tried to eliminate the Gaussian noise, and the median filter is fixed to diminish the spike-like noise. The synthetic dataset and field data examples (from an oil field in the southwest of Iran) have been employed to demonstrate that random noise reduction can be attained, while the signal content will not be destroyed considerably. The final results indicate the authority of...
Sensors
Addressing insufficient and irregular sampling is a difficult challenge in seismic processing and imaging. Recently, rank reduction methods have become popular in seismic processing algorithms for simultaneous denoising and interpolating. These methods are based on rank reduction of the trajectory matrices using truncated singular value decomposition (TSVD). Estimation of the ranks of these trajectory matrices depends on the number of plane waves in the processing window; however, for the more complicated data, the rank reduction method may fail or give poor results. In this paper, we propose an adaptive weighted rank reduction (AWRR) method that selects the optimum rank in each window automatically. The method finds the maximum ratio of the energy between two singular values. The AWRR method selects a large rank for the highly curved complex events, which leads to remaining residual errors. To overcome the residual errors, a weighting operator on the selected singular values minimi...
A021 COMPRESSION DENOISING: USING SEISMIC COMPRESSION FOR UNCOHERENT NOISE REMOVAL
2001
Summary Wavelet related techniques have been proved successful in many seismic processing applications, such as filtering or compression. While seismic data compression is not yet widely accepted, we propose a compression based on filter banks as a means to remove uncoherent noise from seismic data, and thus improve the SNR. Results are demonstrated on synthetic data.