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 …

Seismic data recovery from data with missing traces on otherwise regular acquisition grids
forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery …

In this work, a new human face recognition algorithm based on bidirectional two
dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) …

Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing
procedures, the noise attenuation is important. We propose an adaptive random noise …

Seismic denoising with nonuniformly sampled curvelets Page 1 Recently introduced, curvelets
are among the latest members of a growing family of multiscale—and now multidirec …

Many seismic exploration techniques rely on the collection of massive data volumes that are
subsequently mined for information during processing. Although this approach has been …

Accurate removal of surface-related multiples remains a challenge in many cases. To
overcome typical inaccuracies in current multiple-removal techniques, we have developed a …

Representation of a signal in a sparse way is a useful and popular methodology in signal-
processing applications. Among several widely used sparse transforms, dictionary learning …

Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that
determine image quality. Curvelets, little “plane waves,” complete with oscillations in one …

Interpolation and random noise removal is a pre-requisite for multichannel techniques
because the irregularity and random noise in observed data can affect their performances …