# A novel attention model for salient structure detection in seismic volumes

arXiv: https://arxiv.org/abs/2201.06174 · HTML: https://mlsanity.com/paper/2201.06174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Salient Object Detection: A Benchmark](https://mlsanity.com/paper/1501.02741/) (2015)
- [Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective](https://mlsanity.com/paper/1812.08756/) (2018)
- [The role of visual saliency in the automation of seismic interpretation](https://mlsanity.com/paper/1812.11960/) (2018)
- [A comparative study of texture attributes for characterizing subsurface structures in seismic volumes](https://mlsanity.com/paper/1812.08263/) (2018)
- [SalSi: A new seismic attribute for salt dome detection](https://mlsanity.com/paper/1901.02937/) (2019)
- [Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning](https://mlsanity.com/paper/1812.11109/) (2018)
- [Characterization of migrated seismic volumes using texture attributes: a comparative study](https://mlsanity.com/paper/1901.10909/) (2019)
- [Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection](https://mlsanity.com/paper/1901.01550/) (2019)
- [Tensor-based subspace learning for tracking salt-dome boundaries](https://mlsanity.com/paper/1901.02921/) (2019)
- [Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons](https://mlsanity.com/paper/1901.11095/) (2019)

## Built on by (most cited first, 2 of 2)

- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior](https://mlsanity.com/paper/2505.08585/) (2025)

## Abstract (arXiv)

A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimensional spectral projection, which decomposes local spectra into three distinct components, each depicting variations along different dimensions of the data. Subsequently, a novel directional center-surround attention model is proposed to incorporate directional comparisons around each voxel for saliency detection within each projected dimension. Next, the resulting saliency maps along each dimension are combined adaptively to yield a consolidated saliency map, which highlights various structures characterized by subtle variations and relative motion with respect to their neighboring sections. A priori information about the seismic data can be either embedded into the proposed attention model in the directional comparisons, or incorporated into the algorithm by specifying a template when combining saliency maps adaptively. Experimental results on two real seismic datasets from the North Sea, Netherlands and Great South Basin, New Zealand demonstrate the effectiveness of the proposed algorithm for detecting salient seismic structures of different natures and appearances in one shot, which differs significantly from traditional seismic interpretation algorithms. The results further demonstrate that the proposed method outperforms comparable state-of-the-art saliency detection algorithms for natural images and videos, which are inadequate for seismic imaging data.