物探化探计算技术2024，Vol.46Issue(3)：272-283,12.DOI:10.3969/j.issn.1001-1749.2024.03.03
基于LOFUnet深度卷积神经网络低序级断层多属性识别方法
Multi-attribute recognition method for low-order faults based on LOFUnet deep convolutional neural network
摘要
Abstract
Low-order faults control traps and hydrocarbon enrichment,which are significant for oil and gas exploration and development.However,its identification and description are complicated and inefficient,which seriously restricts such res-ervoirs'exploration and development process.With the development of artificial intelligence,deep learning provides a new way to identify low-order faults.LOFUnet network is an improvement based on UNet,which can obtain more features of low-order fault information in the sample.In this paper,a new fault body is obtained through the fusion of variance attribute,dip attribute,and amplitude attribute,and the LOFUnet network is constructed to identify low-order faults.The network in this paper can obtain more low-order fault features at the encoder end,solve the problem of gradient disappearance,improve the model's convergence speed,enhance the model's stability,and improve the accuracy and efficiency of low-order fault detec-tion.The forward simulation and actual seismic data are used to test the UNet and LOFUnet models,respectively.The results show that the multi-attribute recognition method of low-order faults based on the LOFUnet depth convolution neural net-work can extract more information and improve the accuracy of low-order fault recognition.关键词
低序级断层/Unet网络/LOFUnet网络/多属性识别/模型试算Key words
low-order fault/Unet network/LOFUnet network/multi-attribute identification/model calculation分类
地质学引用本文复制引用
马玉歌,苏朝光,丁仁伟,颜世磊,张玉洁,韩天娇,闫绘栋..基于LOFUnet深度卷积神经网络低序级断层多属性识别方法[J].物探化探计算技术,2024,46(3):272-283,12.基金项目
中国石化胜利油田分公司项目(YKY2405) （YKY2405）
