A review of artificial intelligence-based seismic first break picking methods
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.
国家科技期刊平台
NSTL
万方数据
