Two abstracts accepted in SEG 2019


Two abstracts were accepted for presentation in SEG’s annual meeting in San Antonio, TX.

  1. M. Alfarraj and G. AlRegib, “Semi-supervised Learning for Acoustic Impedance Inversion,” Expanded Abstracts of the SEG Annual Meeting , San Antonio, TX, Sep. 15-20, 2019.

  2. A. Mustafa, M. Alfarraj, and G. AlRegib, “Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network,” Expanded Abstracts of the SEG Annual Meeting , San Antonio, TX, Sep. 15-20, 2019.

Looking forward to meeting you in SEG if you’re attending.

Paper Published in the special section of TLE

Our paper titled "Multiresolution Analysis and Learning for Computational Seismic Interpretation" was published in the special section of The Leading Edge: Advancements in image processing. 



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 improve the labeling performance compared to using seismic amplitude alone. Moreover, directional multiresolution attributes, such as the curvelet transform, are more effective than the nondirectional attributes in distinguishing different subsurface structures in large seismic data sets and can greatly help the interpretation process.