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
易思梦 1唐东林 1赵云亮 1李恒辉 1丁超2
作者信息
- 1. 西南石油大学机电工程学院,四川成都 610500
- 2. 成都工业学院智能制造学院,四川成都 611730
- 折叠
摘要
地震初至拾取可以提供关于地下结构和地震活动的重要信息,对于地震勘探和地质研究具有重要意义.在低信噪比数据上如何自动准确地拾取初至波备受关注.文章综述了基于人工智能的地震拾取方法,对聚类、支持向量机、反向传播神经网络、卷积神经网络和循环神经网络等五类方法的原理、特点和发展历程进行了阐述.聚类、支持向量机和反向传播神经网络相对直观和可解释,但需要人工提取特征;卷积神经网络和循环神经网络能自主学习地震数据特征,但需要大量有标签数据驱动.最后,探讨了地震初至拾取所面临的挑战和未来的研究方向,指出极低信噪比初至拾取的实时性和网络的轻量化需要继续推进.
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引用本文复制引用
基金项目
四川省科技计划(2021YFSY0024)
成都市技术创新研发项目(2018-YF05-00201-GX)
四川省市场监督管道局科技项目(SCSJZ2022007)
出版年
2024
国家科技期刊平台
NSTL
万方数据