Extracting Dispersion Curves Using Semantic Segmentation of Fully Convolutional Networks
High-frequency surface-wave methods have been widely used for surveying s-wave velocities in near surface both active and positive seismic methods.A key step in high-frequency surface-wave methods is to acquire the dispersion curves in images of surface waves in the frequency-velocity domain.The traditional way to acquire the dispersion curves is to identify the dispersion energy and manually pick phase velocities by following peaks at different frequencies,which is a time consuming task.We propose a novel method to extract dispersion curves using semantic segmentation of fully convolutional networks (FCN) without manual intervention.We selected Resnet34 model as a foundation network to build the FCN for dispersion energy areas segmentation.Results show that the FCN is suitable and efficient for dispersion energy segmentation tasks.Dispersion curves can be extracted from the segmented dispersion energy areas easily and accurately.
Surface waves Dispersion curves Semantic segmentation Convolutional networks
Tianyu Dai Jianghai Xia Ling Ning
Subsurface Imaging and Sensing Laboratory, Institute of Geophysics and Geomatics, China University o School of Earth Sciences, Zhejiang University, Hangzhou 310027
国际会议
杭州
英文
150-155
2018-06-10（万方平台首次上网日期，不代表论文的发表时间）