Attenuation of seismic random noise is considered an important processing step to enhance
the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random …

Geophysical data interpolation has attracted much attention in the past decades. While a
variety of methods are well established for either regularly sampled or irregularly sampled …

We have developed a novel approach to attenuate random noise based on local signal-and-
noise orthogonalization. In this approach, we first removed from a seismic section using one …

Predictive filtering (PF) in the frequency domain is one of the most widely used denoising
algorithms in seismic data processing. PF is based on the assumption of linear or planar …

Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …

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 …

Effective and efficient arrival picking plays an important role in microseismic and earthquake
data processing and imaging. Widely used short-term-average long-term-average ratio …

The Cadzow rank-reduction method can be effectively utilized in simultaneously denoising
and reconstructing 5-D seismic data that depend on four spatial dimensions. The classic …

In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random
noise and preserve the coherent seismic signal. The input data are divided into several …

ABSTRACT A key step in sparsifying signals is the choice of a sparsity-promoting dictionary.
There are two basic approaches to design such a dictionary: the analytic approach and the …