石油地球物理勘探2025，Vol.60Issue(3)：610-617,8.DOI:10.13810/j.cnki.issn.1000-7210.20240337
无监督地层倾角智能计算方法及应用效果
Unsupervised intelligent stratigraphic dip calculation and its application effects
摘要
Abstract
In seismic geometric attributes,stratigraphic dip is the basis for calculating attributes such as curva-ture and coherence,and has been widely applied in seismic data interpretation.However,traditional multi-win-dow scanning algorithms are inefficient,and the existing intelligent algorithms based on end-to-end supervised training are constrained in their generalization and transferability due to the diversity of seismic data.There-fore,this paper proposes an unsupervised training method for intelligent dip calculation using deep neural net-works.This method is based on a three-dimensional convolutional neural network(3D CNN)and achieves un-supervised optimization of the deep neural network by establishing and solving an optimization objective for the structure tensor.It does not require the prior creation of a large number of labels,and combined with transfer learning and fine-tuning for actual work area,achieves efficient and stable 3D dip angle calculation based on the efficient computation of seismic feature vectors.Extensive applications on models and actual data have shown that this intelligent method significantly improves computational efficiency while maintaining stable computation results.Specifically,the geometric curvature obtained based on intelligent dip calculation results exhibits more advantages in expressing fracture information.关键词
深度神经网络/无监督学习/地震特征向量/地层倾角Key words
deep neural network(DNN)/unsupervised learning/seismic feature vector/stratigraphic dip分类
地质学引用本文复制引用
郭锐,文若冲,梁琰,姚燕飞,陶春峰,高英楠..无监督地层倾角智能计算方法及应用效果[J].石油地球物理勘探,2025,60(3):610-617,8.基金项目
本项研究受中国石油集团科学研究与技术开发项目"地震处理解释关键新技术研究与智能化软件研发"(2021ZG03)、中石油东方公司科研项目"智能地震采集处理解释关键技术研究"(01-03-2023)联合资助. （2021ZG03）
