# Characterization of migrated seismic volumes using texture attributes: a comparative study

arXiv: https://arxiv.org/abs/1901.10909 · HTML: https://mlsanity.com/paper/1901.10909/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

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

- [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)
- [A novel attention model for salient structure detection in seismic volumes](https://mlsanity.com/paper/2201.06174/) (2022)

## Abstract (arXiv)

In this paper, we examine several typical texture attributes developed in the image processing community in recent years with respect to their capability of characterizing a migrated seismic volume. These attributes are generated in either frequency or space domain, including steerable pyramid, curvelet, local binary pattern, and local radius index. The comparative study is performed within an image retrieval framework. We evaluate these attributes in terms of retrieval accuracy. It is our hope that this comparative study will help acquaint the seismic interpretation community with the many available powerful image texture analysis techniques, providing more alternative attributes for their seismic exploration.