ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion

no code implementations • 13 Feb 2023 • YiMin Dou, Kewen Li, Wenjun Lv, Timing Li, Hongjie Duan, Zhifeng Xu

In the field data with two or three well logs, only the methods based on the components proposed in this paper were able to achieve reasonable results.

Contrastive Learning Pseudo Label +3

MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing

1 code implementation • 7 Apr 2022 • YiMin Dou, Kewen Li, Hongjie Duan, Timing Li, Lin Dong, Zongchao Huang

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 such as high-ratio random discrete missing, continuous missing and missing in fault-rich or salt body surveys.

MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation

1 code implementation • 11 Oct 2021 • YiMin Dou, Kewen Li, Jianbing Zhu, Timing Li, Shaoquan Tan, Zongchao Huang

Recently, training 3D fault segmentation using sparse manual 2D slices is thought to yield promising results, but manual labeling has many false negative labels (abnormal annotations), which is detrimental to training and consequently to detection performance.

Fault Detection Image Segmentation +2