利用地震资料进行断层识别技术的研究进展  

Research progress of fault identification technology based on seismic data

作　　者：张林[1] 孟原 祁丽莎[1] 阿迪力江·阿不都萨拉木 代军[1] 李昂 张丽艳 ZHANG Lin;MENG Yuan;QI LiSha;ALIMUJIANG Abudusalamu;DAI Jun;LI Ang;ZHANG LiYan(Exploration and Development Research Institute,Xinjiang Oilfield Company,PetroChina,Karamay 834000,China;China University of Petroleum-Beijing at Karamay,Karamay 834000,China)

机构地区：[1]中国石油新疆油田分公司勘探开发研究院,克拉玛依834000 [2]中国石油大学(北京)克拉玛依校区,克拉玛依834000

出　　处：《地球物理学进展》2025年第1期208-219,共12页Progress in Geophysics

基　　金：中国石油大学(北京)克拉玛依校区科研启动基金项目“基于地震岩石物理的页岩地层可压性评价研究”(XQZX20240015);“页岩油勘探中宽方位地震各向异性分析与校正方法研究”(XQZX20240029);新疆维吾尔自治区自然科学基金杰出青年科学基金项目“塔里木盆地中部超深层地震成像机理及全方位速度建模方法研究”(2024D01E08);新疆维吾尔自治区“天池英才”创新领军人才计划项目联合资助及大学生创新创业训练计划联合资助.

摘　　要：随着油气勘探开发技术的深入发展,传统人工断层解释主观性强、工作量大、效率低等缺陷,使其无法满足断层的高效率识别和精细化解释需求.本文在调研国内外大量文献的基础上,探讨多种代表性断层识别技术的优势、应用范围与局限性分析等,基于此可大致划分为单一地震属性、多属性融合和人工智能为代表的三大类断层识别技术.单一属性断层识别技术(频谱分解、相干体、方差体等)主要应用于地震勘探早期的大断层识别.小断层识别方面,RBG多属性融合技术具有独特的优势,通过改变不同属性权重突显断层构造信息,降低干扰与多解性.大数据时代的到来,基于人工智能的断层识别技术得到广泛应用.蚂蚁体追踪技术提高断层识别精度,但存在多解性强、抗噪能力低等问题.此后,卷积神经网络、残差神经网络等基于图像分类的模型和全卷积神经网络、U-Net等基于语义分割的模型被大量应用于断层识别的研究,推动断层识别自动化、智能化的进一步发展.本文归纳总结并对比多种断层识别技术,提出未来发展方向,为今后油气勘探中利用地震资料进行断层解释与识别提供新思路.With the further evolution of oil and gas exploration and development technology,the traditional artificial fault interpretation has some defects such as strong subjectivity,heavy workload and low efficiency,which cannot meet the needs of efficient identification of faults on seismic data and the exact realization of structural characteristics in the study area interpretation needs.This article explores the process,advantages,application scope,and limitations of various representative fault identification technologies found on a large number of domestic and foreign literature.Based on this,it can be roughly divided into three categories of fault identification technologies represented by single seismic attribute,multi attribute fusion,and artificial intelligence.Single attribute fault interpretation techniques mainly include spectral decomposition,coherence volume,variance volume,etc.These techniques and methods are mainly applied in the early stage of seismic exploration,and are relatively effective for the identification of large faults.In terms of small fault recognition,the seismic multi-attribute fusion technology based on RBG attribute fusion has unique advantages.By changing the weight of different attributes,the structural information of the fault is highlighted,so as to reduce the interference and reduce interference and ambiguity.With the advent of the big data era,fault identification technology based on artificial intelligence has been widely used.Ant body tracking belongs to the early artificial intelligence fault identification technology,which partly improves the accuracy of fault identification,but there are still some problems such as strong multi-solution and low anti-noise ability.Since then,neural networks have been introduced into seismic data processing and interpretation,mainly including image classification and semantic segmentation.In particular,residual neural networks,convolutional neural networks,fully convolutional neural networks and U-Net have been widely used in the research of fau

关 键 词：断层识别 地震属性 融合 人工智能 神经网络 

分 类 号：P631[天文地球—地质矿产勘探]