activity
20232026
most citedThe Nash-MTL-STCN Method For Prestack Three-Parameter Inversion

7 citations · 14 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging

Junheng Peng, Yong Li, Mingwei Wang +1

Acoustic impedance imaging is a fundamental yet severely ill-posed problem in subsurface analysis: the seismic wavelet is unknown, observations are band-limited, and labeled well-l…

physics.geo-ph2025

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…

physics.geo-ph2025

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…

physics.geo-ph2025

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…

physics.geo-ph2025★ 1 cited

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…

eess.IV2024

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…

physics.geo-ph2024

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…

physics.geo-ph2024

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…

physics.geo-ph2024

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…

physics.geo-ph2024

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…

physics.geo-ph2024★ 7 cited

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…

physics.geo-ph2024★ 1 cited

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…

physics.geo-ph2024★ 2 cited

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…

physics.geo-ph2023★ 3 cited

Seismic Data Strong Noise Attenuation Based on Diffusion Model and Principal Component Analysis

Junheng Peng, Yong Li, Zhangquan Liao +2

Seismic data noise processing is an important part of seismic exploration data processing, and the effect of noise elimination is directly related to the follow-up processing of da…