• 责任者：Pang Guoyin (Key Laboratory of Petroleum Resources Research ; Institute of Geology and Geophysics ; Chinese Academy of Sciences ; Lanzhou 730000 ; China) ; Tian Bing
• 刊名：Journal of Earth Sciences and Environment
• 出版年：2013
• 卷期：35(3)
• 关键词：沉积微相 ; sedimentary microfacies ; 东海盆地 ; East China Sea Basin
• 页码：p.75-82
• 图表：6 illus., 4 tables, 27 refs.
• 分类号：0803
• issnNo：ISSN1672-6561
• isbnNo：CN61-1423/P

     Sedimentary microfacies recognition of Yueguifeng Formation in Lishui-Jiaojiang sag of East China Sea Shelf Basin is circumscribed because of the shortage of offshore drilling well core. Probabilistic neural network （PNN） was used for sedimentary microfacies recognition of Yueguifeng Formation. Firstly, based on the response analysis of seismic facies-sedimentary facies and principal component analysis of logging curve, relationship between seismic facies and sedimentary facies was coupling and corresponding, so that seismic facies was selected as the category independent variable from the input item parameters of PNN, meanwhile, natural gamma, self potential, acoustic, density logging, compensated neutron and caliper logging curves, which were helpful to distinguish different sedimentary microfacies, were taken as the numerical independent variables from the input item parameters of PNN; secondly, 2 199 learningsamples were used to train PNN, the optimal smoothing factors of variables were searched after 65 tests, and the discrimination model for 20 kinds of sedimentary microfaeies in study area was established; finally, the PNN established was used to identify sedimentary microfacies. The results show that compared with the result of well core analysis, the accuracy rate of sedimentary microfacies identified by PNN is more than 90%, so that it is feasible to identify sedimentary microfacies from the non-cored area by PNN.