Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

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 …

The cost of obtaining a complete porosity value using traditional coring methods is relatively
high, and as the drilling depth increases, the difficulty of obtaining the porosity value also …

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 …

Seismic prospecting is a common method used in oil and gas resource exploration.
However, due to the limitations of current collection techniques, seismic records acquired in …

The seislet transform uses a prediction operator that is connected to the local slope or
frequency of seismic events. We have combined the 1D nonstationary seislet transform with …

Random noise attenuation is one of the most essential steps in seismic signal processing.
We propose a novel approach to attenuate seismic random noise based on deep …

Seismic random noise attenuation is a key step in seismic data processing. The random
seismic data recorded by the detector tends to have strong noise, and this noisy seismic …

A sparse dictionary is more adaptive than a sparse fixed-basis transform since it can learn
the features directly from the input data in a data-driven way. However, learning a sparse …