.png){kind=icon}
.png){kind=icon}
Return
Subsurface Structure Analysis Using Computational Interpretation and Learning A visual signal processing perspective
.png){kind=icon}
OA
AI
DOI：10.1109/MSP.2017.2785979.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
SEISMIC ATTRIBUTES
IMAGE SEGMENTATION
TEXTURE ANALYSIS
FAULT SURFACES
GAS CHIMNEY
COHERENCE
FACIES
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W