Abstract
As part of the 4th annual Smoky Mountains Data Challenge hosted by Oak Ridge National Laboratory, we sought to quantify uncertainty in subsurface exploration of the underground to facilitate decision-making. To provide some context, in the collection of seismic data, sounds waves are transmitted into the ground and their reflections recorded by a receiver. However, due to inconsistencies of the subsurface medium, accurate localization of underground layers is difficult without directly digging down to confirm. To combat this issue, we used several statistical and computer vision to quantify uncertainty of seismic data images by labelling each pixel of a seismic survey (realistic models of subsurface density) to indicate its volatility. After thorough analysis, we could conclude that not one “good” metric exists to accomplish our de- fined goal; uncertainty is defined differently depending on the specific methods one employs. Every uncertainty map that was generated using a unique technique highlighted distinct areas of the seismic surveys. More experimentation and feedback from experts are needed to identify what optimal combination of these (or other) techniques would be best to arrive at the best measurement by which to measure subsurface uncertainty.
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References
Al-Taie, A.A.: Uncertainty estimation and visualization in segmenting uni-and multi-modal medical imaging data. Ulm University, Ulm, Germany (2015)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Wang, Z., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Gray, K., Grossman, M., Yusifov, A.: Using machine learning to understand uncertainty in subsurface exploration. Smokey Mountain Data Challenge Problem Descriptions (2020)
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Bae, J., Sheng, J. (2020). Using Statistical Analysis and Computer Vision to Understand Uncertainty in Subsurface Exploration. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_36
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DOI: https://doi.org/10.1007/978-3-030-63393-6_36
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