7 papers · 1 filter
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…
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…
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…
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…