基于宽度学习的横波速度预测OA

Shear-wave velocity is a key parameter for pre-stack seismic inversion and reservoir characterization.However,due to the technical and cost constraints of both direct and indirect measurement methods,it is quite difficult to obtain in practice.Therefore,a prediction method is proposed based on the broad learning system(BLS).First,appropriate well-log data are selected and pre-processed through denoising and correlation analy-sis.Second,a BLS neural network structure comprising mapping nodes and enhancement nodes is constructed to complete the BLS process.Finally,well-log data from the two typical wells Y301 and Y302 in the Y block of the Junggar Basin are used to construct a data set of machine learning.Two contrast experiments are designed and compared with curve fitting and deep learning system to verify the stability and generalization of BLS.The ac-tual results show that the proposed BLS-based shear wave velocity prediction method can reduce training time while achieving prediction accuracy,providing a new neural network option for shear wave velocity,petro-leum,and relevant reservoir parameter prediction.