6 papers · 1 filter
Strong noise attenuation of seismic data based on Nash equilibrium
Mingwei Wang, Yingtian Liu, Junheng Peng +2
Seismic data acquisition is often affected by various types of noise, which degrade data quality and hinder subsequent interpretation. Recovery of seismic data becomes particularly…
The Nash-MTL-STCN Method For Prestack Three-Parameter Inversion
Yingtian Liu, Yong Li, Huating Li +3
Deep learning (DL) techniques have been widely used in prestack three-parameter inversion to address its ill-posed problems. Among these DL techniques, Multi-task learning (MTL) me…
DCMSA: Multi-Head Self-Attention Mechanism Based on Deformable Convolution For Seismic Data Denoising
Wang Mingwei, Li Yong, Liu Yingtian +2
When dealing with seismic data, diffusion models often face challenges in adequately capturing local features and expressing spatial relationships. This limitation makes it difficu…
High-resolution closed-loop seismic inversion network in time-frequency phase mixed domain
Yingtian Liu, Yong Li, Junheng Peng +2
Thin layers and reservoirs may be concealed in areas of low seismic reflection amplitude, making them difficult to recognize. Deep learning (DL) techniques provide new opportunitie…
Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network
Wen Feng, Yong Li, Yingtian Liu +1
Seismic impedance inversion is a widely used technique for reservoir characterization. Accurate, high-resolution seismic impedance data form the foundation for subsequent reservoir…
An anti-noise seismic inversion method based on diffusion model
Yingtian Liu, Yong Li, Xingan Hao +3
Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack…