4 papers
A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning
Hongtao Wang, Jiandong Liang, Lei Wang +4
Residual moveout (RMO) provides critical information for travel time tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equati…
Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
Xiaoli Wei, Chunxia Zhang, Hongtao Wang +5
Accurate interpolation of seismic data is crucial for improving the quality of imaging and interpretation. In recent years, deep learning models such as U-Net and generative advers…
DSU-Net: Dynamic Snake U-Net for 2-D Seismic First Break Picking
Hongtao Wang, Rongyu Feng, Liangyi Wu +4
In seismic exploration, identifying the first break (FB) is a critical component in establishing subsurface velocity models. Various automatic picking techniques based on deep neur…
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Hongtao Wang, Li Long, Jiangshe Zhang +3
Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2…