Home » blog_post » Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

Our article titled: “Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective” has been published in the March 2018 issue of the Signal Processing Magazine.

Full article:  http://ieeexplore.ieee.org/abstract/document/8312469/

Abstract: Understanding Earth’s subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth’s subsurface structures. In this article, we first summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine-learning algorithms.

SPM2018_blog_1

An analogy between seismic interpretation, natural scene analysis, and medical imaging analysis. MRI: magnetic resonance imaging. CT: computed tomography.

 

SPM2018_blog_2

Subsurface structures and their corresponding interpretation methods. GLCM: gray-level co-occurrence matrix.

 

 


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