Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation

Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient removal, effective signal preservation and recovery, reasonable processing time with a minimum distortion event deterioration are properties of desired suppression algorithm. There various available that more or less have these properties. We aim obtain by assuming 3-D as tensor order three increasing its dimension 4-D continuous wavelet transform (CWT). First, we map block smaller blocks estimate the low-rank component. The CWT is calculated along third extract singular values their related left/right vectors domain. Afterward, component extracted using optimized coefficients for each value. Thresholding applied domain calculate coefficients. Two synthetic field examples considered performance evaluation proposed method, results were compared competitive random methods, such optimum shrinkage value decomposition, iterative thresholding, matching algorithms. Qualitative quantitative comparison method other indicates efficiently eliminates from