Application of sparse S transform network with knowledge distillation in seismic attenuation delineation

扫码查看

原文链接

NETL
NSTL
万方数据

外文摘要：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.

外文关键词：

S transformDeep learningKnowledge distillationTransfer learningSeismic attenuation delineation

作者：

Nai-Hao Liu、Yu-Xin Zhang、Yang Yang、Rong-Chang Liu、Jing-Huai Gao、Nan Zhang

展开 >

作者单位：

School of Information and Communications Engineering,Xi'an Jiaotong University,Xi'an,710049,Shaanxi,China

School of Software Engineering,Xi'an Jiaotong University,Xi'an,710049,Shaanxi,China

PetroChina Research Institute of Petroleum Exploration and Development(RIPED),CNPC,Beijing 100083,China

Research Institute of Exploration and Development,Yumen Oilfield Company,CNPC,Jiuquan,735019,Gansu,China

展开 >

出版年：

2024

DOI：

10.1016/j.petsci.2024.03.002

石油科学(英文版)

中国石油大学（北京）

石油科学(英文版)

影响因子：0.88

ISSN：1672-5107

年,卷(期)：2024.21(4)