Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data.Traditionally,the layering algorithm mainly use the der...Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data.Traditionally,the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference.However,measurement error or resolution mismatch may lead to misjudgment of the boundary.In view of the shortcomings of traditional methods,this paper presents an automatic layering algorithm of array induction logging curves based on deep learning.In this algorithm,a locally connected convolution neural network is used,and the generalization ability of the network is improved by enlarging the training set,optimizing the window length and threshold,and strengthening the layering effect.Simulation and field data show the eff ectiveness of the proposed algorithm.展开更多
基金the National Nature Science Foundation of China(No.41604123)。
文摘Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data.Traditionally,the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference.However,measurement error or resolution mismatch may lead to misjudgment of the boundary.In view of the shortcomings of traditional methods,this paper presents an automatic layering algorithm of array induction logging curves based on deep learning.In this algorithm,a locally connected convolution neural network is used,and the generalization ability of the network is improved by enlarging the training set,optimizing the window length and threshold,and strengthening the layering effect.Simulation and field data show the eff ectiveness of the proposed algorithm.