石油地球物理勘探2026，Vol.61Issue(1)：17-23,7.DOI:10.13810/j.cnki.issn.1000-7210.20250137
基于宽度学习的横波速度预测
Shear wave velocity prediction based on broad learning system
摘要
Abstract
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.关键词
宽度学习算法/曲线拟合/深度学习算法/横波速度预测Key words
broad learning system/curve fitting/deep learning system/shear wave velocity prediction分类
天文与地球科学引用本文复制引用
林雨峰,关业坤,高刚,吴广能,曹潇宇,桂志先..基于宽度学习的横波速度预测[J].石油地球物理勘探,2026,61(1):17-23,7.基金项目
本项研究受中国博士后科学基金第77批面上项目"基于垂向地层层序与空间反射结构双约束的非稳态反射系数反演方法研究"(2025M770452)、2025年度中国博士后科学基金会与湖北省联合(特别资助)项目"基于地震、测井和地质多信息融合的非稳态反射系数反演方法研究"(2025T044HB)和油气资源与勘探技术教育部重点实验室(长江大学)开放基金项目"频率域稳定化的Q补偿逆时偏移方法研究"(K2023-04)联合资助. （2025M770452）
