Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 12
• Main Authors: Yin, Zhen, Amaru, Maisha, Wang, Yizheng, Li, Lewis, Caers, Jef
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayesian analysis; Boreholes; Data models; Earth resources; High resolution; High-resolution lithological modeling; Learning; Lithofacies; Lithology; Markov chain Monte Carlo (McMC); Mathematical models; Monte Carlo methods; Probability theory; Proposals; Resolution; Resource development; seismic; Seismic data; Simulation; Solid modeling; Statistics; Three dimensional models; Three-dimensional displays; Turbidites; Uncertainty; uncertainty quantification

Building high-resolution lithological models using seismic data can facilitate decision-makings for earth resources development, but they are subject to considerable uncertainties due to the limited seismic resolution. Traditionally, high-resolution lithological models are built deterministically or...