The amplitude-variation-with-angle (AVA) inversion for seismic data has been widely used
for hydrocarbon detection in exploration seismology. Traditional AVA inversion quantitatively …

Deep learning (DL) seismic simulations have become a leading-edge field that could
provide an effective alternative to traditional numerical solvers. We have developed a small …

Deep learning is a promising approach to velocity model building because it has the
potential of processing large seismic surveys with minimal resources. By leveraging large …

Accurate removal of surface-related multiples remains a challenge in shallow-water cases.
One reason is that the success of surface-related multiple estimation (SRME)-related …

The process of seismic multiple removal in oil seismic exploration is crucial for the imaging
of underground structures with primary reflections. The inclusion of prediction and …

Seismic multiples in marine seismic data can affect the identification of oil and gas
reservoirs. The efficiency of traditional multiple suppression methods, such as the Radon …

Surface-related multiples are generally removed as noise. To suppress surface-related
multiples, we propose an unsupervised deep neural network approach based on ensemble …

Producing reliable acoustic subsurface velocity models still remains the main bottleneck of
the oil and gas industry's traditional imaging sequence. In complex geologic settings, the …

有效压制多次波一直是地震勘探中的难点问题. 尽管已发展了多种多次波压制方法,
但仍存在多次波压制不全, 计算耗时长等缺陷, 使得应对复杂地质地震数据多次波压制具有挑战 …

Multiple suppression is a very important step in seismic data processing. To suppress
surface-related multiples, we develop a self-supervised deep neural network method based …