The K-SVD algorithm has been successfully utilized for adaptively learning the sparse
dictionary in 2D seismic denoising. Because of the high computational cost of many SVDs in …

The seislet transform has been demonstrated to have a better compression performance for
seismic data compared with other well-known sparsity promoting transforms, thus it can be …

Simultaneous-source shooting can help tremendously shorten the acquisition period and
improve the quality of seismic data for better subsalt seismic imaging, but at the expense of …

Strong noise in seismic data seriously affects many steps in seismic data processing and
imaging. While most traditional methods depend on carefully tuned input parameters by …

The high-order Radon transform is adopted to eliminate incoherent noise that appears in
common receiver gathers when simultaneous source data are acquired. An iterative scheme …

We have proposed a new denoising method for the simultaneous noise reduction and
preservation of seismic signals based on variational mode decomposition (VMD). VMD is a …

Low-frequency noise is one of the most common types of noise in seismic and microseismic
data. Conventional approaches, such as the high-pass filtering method, utilize the low …

The K-SVD algorithm has been successfully utilized for adaptively learning the sparse
dictionary in 2-D seismic denoising. Because of the high computational cost of many …

It has been previously shown that blended simultaneous-source data can be successfully
separated using an iterative seislet thresholding algorithm. In this letter, I combine iterative …

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 …