Shearwave velocity prediction based on broad learning system

Shearwave 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 was proposed based on the broad learning system (BLS). First, appropriate well-log data were selected and pre-processed through denoising and correlation analysis. Second, a BLS neural network structure comprising mapping nodes and enhancement nodes was 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 were used to construct a dataset of machine learning. Two contrast experiments were designed and compared with curve fitting and deep learning systems to verify the stability and generalization of BLS. The actual 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, petroleum, and relevant reservoir parameter prediction.