@article{Liu2024, 
author = {Nai-Hao Liu and Yu-Xin Zhang and Yang Yang and Rong-Chang Liu and Jing-Huai Gao and Nan Zhang},
title = {Application of sparse S transform network with knowledge distillation in seismic attenuation delineation},
year = {2024},
journal = {Petroleum Science},
volume = {21},
number = {4},
pages = {2345-2355},
keywords = {S transform, Deep learning, Knowledge distillation, Transfer learning, Seismic attenuation delineation},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2024.03.002},
doi = {10.1016/j.petsci.2024.03.002},
abstract = {Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data. However, it suffers from several inevitable limitations, such as the restricted time-frequency resolution, the difficulty in selecting parameters, and the low computational efficiency. Inspired by deep learning, we suggest a deep learning-based workflow for seismic time-frequency analysis. The sparse S transform network (SSTNet) is first built to map the relationship between synthetic traces and sparse S transform spectra, which can be easily pre-trained by using synthetic traces and training labels. Next, we introduce knowledge distillation (KD) based transfer learning to re-train SSTNet by using a field data set without training labels, which is named the sparse S transform network with knowledge distillation (KD-SSTNet). In this way, we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels. To test the availability of the suggested KD-SSTNet, we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.}
}