Advancements of Deep Learning in Seismic Monitoring and Prediction
This article provides an overview of deep learning methods and their application in earthquake monitoring and prediction.It introduces mainstream methods such as feedforward neural networks,convolutional neural networks,recurrent neural networks,transformer networks,autoencoders,generative adversarial networks,and deep rein-forcement learning networks.The article summarizes their application in phase picking,phase correlation,event detection,earthquake location,signal and event classification,and earthquake prediction.It also discusses the progress,advantages,challenges,and future directions of deep learning in earthquake monitoring and predic-tion.This summary serves as a valuable reference for applying deep learning in earthquake monitoring and predic-tion.
deep learningseismic monitoring and predictionearthquake detectionearthquake locationseismic forecasting
万方数据
维普
