Blog

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.

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

 

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

 

 

From Object Interactions to Fine-grained Video Understanding

(Joint work by Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, Hans Peter Graf)

Video understanding tasks such as action recognition and caption generation are crucial for various real-world applications in surveillance, video retrieval, human behavior understanding, etc. In this work, we present a generic recurrent module to detect relationships and interactions between arbitrary object groups for fine-grained video understanding. Our work is applicable to various open domain video understanding problems. In this work, we validate our method on two video understanding tasks with new challenging datasets: fine-grained action recognition on Kinetics and visually grounded video captioning on ActivityNet Captions.

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Invited SPE Webinar

Successful Leveraging of Human Visual System Modeling and Machine Learning in Computational Seismic Interpretation

In today’s growing complexity of seismic data, in both size and resolution, manual interpretation increasingly relies on computational seismic interpretation (CSI) for more efficient, accurate, and effective interpretation. This webinar will highlight our studies that have focused on leveraging perception and machine learning in creating a set of CSI algorithms and software tools.

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