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A Novel Method to Enhance the Inversion Speed and Precision of the NMR T_(2) Spectrum by the TSVD Based Linearized Bregman Iteration

查看全文 作　　者：Yiguo [1,2,3]Chen;Congjun [1,2]Feng;Yonghong [3]He;Zhijun [3]Chen;Xiaowei [3]Fan;Chao [3]Wang;Xinmin [4]Ge 高影响力作者 机构地区：[1]State Key Laboratory of Continental Dynamics,Northwest University,Xi’an,710069,China;[2]Department of Geology,Northwest University,Xi’an,710069,China;[3]Reseach Institute of Shaanxi Yanchang Petroleum(Group)Co.,Ltd.,Xi’an,710075,China;[4]School of Geosciences,China University of Petroleum,Qingdao,266580,China高影响力机构 出　　处：《Computer Modeling in Engineering & Sciences》索引2023年第9期,共13页高影响力期刊 基　　金：support by the National Nature Science Foundation of China(42174142);CNPC Innovation Found(2021DQ02-0402);National Key Foundation for Exploring Scientific Instrument of China(2013YQ170463). 摘　　要：The low-field nuclear magnetic resonance(NMR)technique has been used to probe the pore size distribution and the fluid composition in geophysical prospecting and related fields.However,the speed and accuracy of the existing numerical inversion methods are still challenging due to the ill-posed nature of the first kind Fredholm integral equation and the contamination of the noises.This paper proposes a novel inversion algorithmto accelerate the convergence and enhance the precision using empirical truncated singular value decompositions(TSVD)and the linearized Bregman iteration.The L1 penalty term is applied to construct the objective function,and then the linearized Bregman iteration is utilized to obtain fast convergence.To reduce the complexity of the computation,empirical TSVD is proposed to compress the kernel matrix and determine the appropriate truncated position.This novel inversion method is validated using numerical simulations.The results indicate that the proposed novel method is significantly efficient and can achieve quick and effective data solutions with low signal-to-noise ratios. 关 键 词：Low field nuclear magnetic resonance linearized bregman iteration truncated singular value decomposition numerical simulations
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