Quantifying natural delta variability using a  multiple-point geostatistics prior uncertainty model

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1002/2016JF003922.


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Céline Scheidt, Anjali M. Fernandes , Chris Paola, Jef Caers


We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that, with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigen-patterns‟ of the natural system. The eigen-pattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.




Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Electrical and Computer Engineering, Engineering, Environmental Engineering, Geology, Geomorphology, Other Civil and Environmental Engineering, Other Engineering, Physical Sciences and Mathematics, Sedimentology, Stratigraphy, Theory and Algorithms


uncertainty, Deltas, autogenic variability, Bayesian approach, delta kinematics, distance-based clustering, eigen patterns, experimental deltas, multi-point geostatistics, training images


Published: 2018-09-30 17:27

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GNU Lesser General Public License (LGPL) 2.1

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