49 citations · 121 across the 6 of their papers we have counts for
6 papers
FaultSSL: Seismic Fault Detection via Semi-supervised learning
Yimin Dou, Minghui Dong, Kewen Li +1
The prevailing methodology in data-driven fault detection leverages synthetic data for training neural networks. However, it grapples with challenges when it comes to generalizatio…
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion
Yimin Dou, Kewen Li, Wenjun Lv +3
The automated interpretation and inversion of seismic data have advanced significantly with the development of Deep Learning (DL) methods. However, these methods often require nume…
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification
Kewen Li, Wenlong Liu, Yimin Dou +3
Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning app…
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing
Yimin Dou, Kewen Li, Hongjie Duan +3
The interpolation and reconstruction of missing traces is a crucial step in seismic data processing, moreover it is also a highly ill-posed problem, especially for complex cases su…
MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation
Yimin Dou, Kewen Li, Jianbing Zhu +3
Data-driven fault detection has been regarded as a 3D image segmentation task. The models trained from synthetic data are difficult to generalize in some surveys. Recently, trainin…
Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels
YiMin Dou, Kewen Li, Jianbing Zhu +2
Detection faults in seismic data is a crucial step for seismic structural interpretation, reservoir characterization and well placement. Some recent works regard it as an image seg…