7 citations · 8 across the 10 of their papers we have counts for
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Physics-Informed Cross-Learning for Seismic Acoustic Impedance Inversion and Wavelet Extraction
Junheng Peng, Xiaowen Wang, Yingtian Liu +2
Seismic acoustic impedance inversion is one of the most challenging tasks in geophysical exploration. Many studies have proposed the use of deep learning for processing; however, m…
Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion
Junheng Peng, Yingtian Liu, Xiaowen Wang +2
Seismic acoustic impedance inversion is a challenging problem in geophysical exploration, primarily due to the scarcity of well-logging data and the inherent nonlinearity of the ta…
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…
Semi-Supervised Learning for AVO Inversion with Strong Spatial Feature Constraints
Yingtian Liu, Yong Li, Junheng Peng +1
One-dimensional convolution is a widely used deep learning technique in prestack amplitude variation with offset (AVO) inversion; however, it lacks lateral continuity. Although two…
Prior-Driven Self-Supervised Lightweight Method for Seismic Signal Denoising
Junheng Peng, Yong Li, Yingtian LIu +1
Seismic exploration is currently the most mature approach for studying subsurface structures, yet the presence of noise greatly restricts its imaging accuracy. Previous methods sti…
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…
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…