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.


Attended and Presented at SEG2017

I attended the SEG2017 annual meeting and conference in Houston, TX between 24th and 29th of September. Our research group had more than 5 papers accepted at the conference. 

SEG2017 is on of the best conference, and I always look forward to it. I learned quite a lot from this year's event in different areas. I came back from Houston with more ideas to work on and tools to utilize in my research.