# Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels

arXiv: https://arxiv.org/abs/2105.03857 · HTML: https://mlsanity.com/paper/2105.03857/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Improving the Harmony of the Composite Image by Spatial-Separated Attention Module](https://mlsanity.com/paper/1907.06406/) (2019)

## Built on by (most cited first, 6 of 6)

- [MD Loss: Efficient Training of 3D Seismic Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation](https://mlsanity.com/paper/2110.05319/) (2021)
- [MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing](https://mlsanity.com/paper/2204.03197/) (2022)
- [CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults](https://mlsanity.com/paper/2408.11185/) (2024)
- [ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion](https://mlsanity.com/paper/2302.06441/) (2023)
- [CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification](https://mlsanity.com/paper/2210.04776/) (2022)
- [Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding](https://mlsanity.com/paper/2507.00419/) (2025)

## Abstract (arXiv)

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 segmentation task. The task of image segmentation requires huge labels, especially 3D seismic data, which has a complex structure and lots of noise. Therefore, its annotation requires expert experience and a huge workload. In this study, we present lambda-BCE and lambda-smooth L1loss to effectively train 3D-CNN by some slices from 3D seismic data, so that the model can learn the segmentation of 3D seismic data from a few 2D slices. In order to fully extract information from limited data and suppress seismic noise, we propose an attention module that can be used for active supervision training and embedded in the network. The attention heatmap label is generated by the original label, and letting it supervise the attention module using the lambda-smooth L1loss. The experiment demonstrates the effectiveness of our loss function, the method can extract 3D seismic features from a few 2D slice labels. And it also shows the advanced performance of the attention module, which can significantly suppress the noise in the seismic data while increasing the model's sensitivity to the foreground. Finally, on the public test set, we only use the 2D slice labels training that accounts for 3.3% of the 3D volume label, and achieve similar performance to the 3D volume label training.