Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective
This work addresses subsurface structure analysis for applications like environmental monitoring and resource exploration, but it is incremental as it primarily summarizes existing methods and suggests future directions.
The paper reviews recent advances in analyzing Earth's subsurface structures by applying image processing and computer vision techniques to seismic data, and discusses challenges and potential directions using emerging machine learning algorithms.
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 paper, first, we 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.