Climate change is a severe global threat. Research on climate change and vulnerability to
natural hazards has made significant progress over the last decades. Most of the research …

Seismic data processing algorithms greatly benefit from regularly sampled and reliable data.
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …

Irregularity and coarse spatial sampling of seismic data strongly affect the performances of
processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in …

In the petroleum industry, artificial intelligence has been applied in seismic and logging
interpretation, accurate modeling, optimized drilling operations, well dynamics prediction …

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 …

We explore the potential of neural networks in approximating the action of the
computationally expensive Estimation of Primaries by Sparse Inversion (EPSI) algorithm …

Addressing insufficient and irregular sampling is a difficult challenge in seismic processing
and imaging. Recently, rank reduction methods have become popular in seismic processing …

Deep neural networks (DNNs) can automatically fetch specific features from seismic data,
which can be used in the process of multiple elimination. An extended single-sided …

3D 地震数据不规则采样缺失重建是地震勘探数据处理流程中的重要问题. 本文提出了一种基于
具有保幅特性的非均匀高阶抛物Radon 变换(NHOPRT) 地震数据重建方法 …

The reconstruction of irregular 3D seismic data is an important step in seismic data
processing. This paper proposes a reconstruction method based on Non-uniform High Order …