Seismic impedance inversion based on cycle-consistent generative adversarial network  

作　　者：Yu-Qing Wang Qi Wang Wen-Kai Lu Qiang Ge Xin-Fei Yan 

机构地区：[1]The Institute for Artificial Intelligence,Tsinghua University(THUAI),Beijing,100084,China [2]State Key Laboratory of Intelligent Technology and Systems,Tsinghua University,Beijing,100084,China [3]Beijing National Research Center for Information Science and Technology(BNRist),Tsinghua University,Beijing,100084,China [4]The Department of Automation,Tsinghua University,Beijing,100084,China [5]The Research Institute of Petroleum Exploration and Development,China National Petroleum Corporation(CNPC),Beijing,100083,China

出　　处：《Petroleum Science》2022年第1期147-161,共15页石油科学（英文版）

基　　金：financially supported by the NSFC(Grant No.41974126 and 41674116);the National Key Research and Development Program of China(Grant No.2018YFA0702501);the 13th 5-Year Basic Research Program of China National Petroleum Corporation(CNPC)(2018A-3306)。

摘　　要：Deep learning has achieved great success in a variety of research fields and industrial applications.However,when applied to seismic inversion,the shortage of labeled data severely influences the performance of deep learning-based methods.In order to tackle this problem,we propose a novel seismic impedance inversion method based on a cycle-consistent generative adversarial network(Cycle-GAN).The proposed Cycle-GAN model includes two generative subnets and two discriminative subnets.Three kinds of loss,including cycle-consistent loss,adversarial loss,and estimation loss,are adopted to guide the training process.Benefit from the proposed structure,the information contained in unlabeled data can be extracted,and adversarial learning further guarantees that the prediction results share similar distributions with the real data.Moreover,a neural network visualization method is adopted to show that the proposed CNN model can learn more distinguishable features than the conventional CNN model.The robustness experiments on synthetic data sets show that the proposed method can achieve better performances than other methods in most cases.And the blind-well experiments on real seismic profiles show that the predicted impedance curve of the proposed method maintains a better correlation with the true impedance curve.

关 键 词：Seismic inversion Cycle GAN Deep learning Semi-supervised learning Neural network visualization 

分 类 号：P631.4[天文地球—地质矿产勘探]