4 accepted papers
Existing seismic inversion methods are usually 1D, mainly focusing on improving the vertical resolution of inversion results. A few 2D or 3D inversion techniques are either too simple and lack the consideration of stratigraphic structures, or are too complicated which need to extract dip information…
The transfer learning between the source and target domain has already achieved significant success in machine learning areas. However, the existing methods can not achieve satisfactory result when solving the two distant domains transfer learning problem. In the worst case, it could lead to the neg…
Delineating the seismic fault, which is an important type of geologic structures in seismic images, is a key step for seismic interpretation. Comparing with conventional methods that design a number of hand-crafted features based on the observed characteristics of the seismic fault, convolutional ne…
In this paper, we propose a novel method for generating high-granularity three-dimensional (3D) seismic data from low-granularity data based on tensor sparse coding, which jointly trains a high-granularity dictionary and a low-granularity dictionary. First, considering the high-dimensional propertie…