石油地球物理勘探2024，Vol.59Issue(4)：899-914,16.DOI:10.13810/j.cnki.issn.1000-7210.2024.04.027
基于人工智能的地震初至拾取方法研究进展
A review of artificial intelligence-based seismic first break picking methods
摘要
Abstract
Seismic first break picking plays a crucial role in providing vital information concerning subsurface structures and seismic activities,thereby holding significance for seismic exploration and geological research.The automatic and accurate picking of first-arrival waves from low signal-to-noise ratio data has garnered consi-derable attention from scholars.This paper provides a comprehensive review of artificial intelligence-based methods employed for seismic picking.It presents an in-depth analysis of the principles,characteristics,and de-velopmental trajectory of five distinct types of methods:clustering,support vector machines(SVM),back-propagation neural network(BPNN),convolutional neural networks(CNN),and recurrent neural networks(RNN).Clustering,SVM and BPNN methods demonstrate a relatively intuitive and interpretable nature,al-beit requiring manual feature extraction.Conversely,CNN and RNN methods possess the ability to autono-mously learn seismic data features,yet they rely on substantial volumes of labeled data to facilitate their learn-ing process.Furthermore,this paper discusses the challenges and future research directions of seismic first break picking.Specifically,it emphasizes the imperative need to further advance the real-time capabilities for picking first break under extremely low signal-to-noise ratios and to further develop the lightweight of the net-work.关键词
地震勘探/人工智能/聚类/支持向量机/神经网络Key words
seismic exploration/artificial intelligence/clustering/support vector machine(SVM)/neural network分类
天文与地球科学引用本文复制引用
易思梦,唐东林,赵云亮,李恒辉,丁超..基于人工智能的地震初至拾取方法研究进展[J].石油地球物理勘探,2024,59(4):899-914,16.基金项目
本项研究受四川省科技计划项目"页岩气微震分布式光纤检测技术研究"(2021YFSY0024)、成都市技术创新研发项目"油气装备腐蚀缺陷超声智能检测机器人"(2018-YF05-00201-GX)和四川省市场监督管道局科技计划项目"超声干耦合测厚技术在压力容器检验中的应用研究"(SCSJZ2022007)联合资助. （2021YFSY0024）
