Application of seismic image enhancement technology based on single-frame super-resolution reconstruction
Jingyu Zhao, Lou Li · 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · 2022
The clarity of seismic images has a great impact on the results of seismic data interpretation; therefore, high-resolution seismic images can provide finer geological information for seismic exploration. Coordinate-based Multilayer Perceptron(MLP) is easy for gradient-based optimization and machine learning, and represents a more compact order of magnitude than grid sampling. In this paper, we propose a combination of Random Fourier Features(RFF) and coordinate based MLP method to achieve super-resolution reconstruction of seismic images, taking one image coordinate as input and the output is the RGB value of the pixel at that location. Preprocessing the spatial coordinates of the image with Fourier feature mapping enables the MLP to learn higher frequency detail information and achieve super-resolution reconstruction of a single seismic image. Experiments show that the texture and edge details of the final processed seismic image are much clearer.