7 citations · 11 across the 13 of their papers we have counts for
13 papers
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…
An Efficient Self-supervised Seismic Data Reconstruction Method Based on Self-Consistency Learning
Mingwei Wang, Junheng Peng, Yingtian Liu +1
Seismic exploration remains the most critical method for characterizing subsurface structures in geophysics. However, complex surface conditions often cause a non-uniform distribut…
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…
Adaptive Graded Denoising of Seismic Data Based on Noise Estimation and Local Similarity
Xueting Yang, Yong Li, Zhangquan Liao +2
Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors prop…
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…
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…
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…
Fast Diffusion Model For Seismic Data Noise Attenuation
Junheng Peng, Yong Li, Yingtian Liu +1
Noise is one of the primary sources of interference in seismic exploration. Many authors have proposed various methods to remove noise from seismic data; however, in the face of st…