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

## TL;DR

This study compares various texture attributes derived from image processing techniques to assess their effectiveness in characterizing migrated seismic volumes within an image retrieval framework.

## Contribution

It provides a comprehensive comparison of frequency and space domain texture attributes for seismic volume characterization, aiding seismic interpretation.

## Key findings

- Steerable pyramid and curvelet attributes show high retrieval accuracy.
- Local binary pattern offers computational efficiency.
- The study enhances understanding of texture attribute effectiveness in seismic analysis.

## Abstract

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.