# Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1904.04445 · HTML: https://mlsanity.com/paper/1904.04445/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer](https://mlsanity.com/paper/2109.08024/) (2021)
- [Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition](https://mlsanity.com/paper/2211.04717/) (2022)
- [Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation](https://mlsanity.com/paper/2302.14644/) (2023)
- [Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2403.03512/) (2024)
- [Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection](https://mlsanity.com/paper/2108.09605/) (2021)
- [Generating Data Augmentation samples for Semantic Segmentation of Salt Bodies in a Synthetic Seismic Image Dataset](https://mlsanity.com/paper/2106.08269/) (2021)
- [Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level](https://mlsanity.com/paper/2305.02148/) (2023)
- [Effective Data Selection for Seismic Interpretation through Disagreement](https://mlsanity.com/paper/2406.05149/) (2024)
- [Quantum Feature Pyramid Gating for Seismic Image Segmentation](https://mlsanity.com/paper/2605.15370/) (2026)

## Abstract (arXiv)

Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for identification of hydrocarbon reservoirs and drill path planning. Unfortunately, exact identification of large salt deposits is notoriously difficult and professional seismic imaging often requires expert human interpretation of salt bodies. Convolutional neural networks (CNNs) have been successfully applied in many fields, and several attempts have been made in the field of seismic imaging. But the high cost of manual annotations by geophysics experts and scarce publicly available labeled datasets hinder the performance of the existing CNN-based methods. In this work, we propose a semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training. To reduce error amplification during self-training we propose a scheme which uses an ensemble of CNNs. We show that our approach outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods.