Multiresolution Analysis and Learning for Computational Seismic Interpretation

TL;DR

This paper investigates multiresolution analysis techniques like wavelets, Gabor filters, and curvelets for seismic image characterization, demonstrating their effectiveness in improving subsurface structure labeling in large seismic datasets.

Contribution

It introduces the application of various multiresolution texture attributes to seismic interpretation, highlighting the superior performance of directional methods like curvelets.

Findings

Multiresolution attributes improve seismic structure labeling accuracy.

Directional attributes like curvelets outperform non-directional methods.

Texture-based analysis enhances interpretation of large seismic datasets.

Abstract

We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrated seismic data. Namely, we explore the Gaussian pyramid, the discrete wavelet transform, Gabor filters, and the curvelet transform. These techniques are examined in a seismic structure labeling case study on the Netherlands offshore F3 block. In seismic structure labeling, a seismic volume is automatically segmented and classified according to the underlying subsurface structure using texture attributes. Our results show that multiresolution attributes improved the labeling performance compared to using seismic amplitude alone. Moreover, directional multiresolution attributes, such as the curvelet transform, are more effective than the non-directional attributes in distinguishing different subsurface structures in large seismic datasets, and can greatly help the interpretation process.