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Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone Public Deposited

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Jorden Hayes is a professor of Earth Sciences at Dickinson College.

Grana, Dario, Andrew D. Parsekian, Brady A. Flinchum, Russell P. Callahan, Natalie Y. Smeltz, Ang Li, Jorden L. Hayes, Brad J. Carr, Kamini Singha, Clifford S. Riebe, W. Steven Holbrook. "Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone." Mathematical Geosciences (Article published online June 4, 2022).  https://doi.org/10.1007/s11004-022-10006-0

Understanding the subsurface structure and function in the near-surface groundwater system, including fluid flow, geomechanical, and weathering processes, requires accurate predictions of the spatial distribution of petrophysical properties, such as rock and fluid (air and water) volumetric fractions. These properties can be predicted from geophysical measurements, such as electrical resistivity tomography and refraction seismic data, by solving a rock physics inverse problem. A Bayesian inversion approach based on a Monte Carlo implementation of the Bayesian update problem is developed to generate multiple realizations of porosity and water saturation conditioned on geophysical data. The model realizations are generated using a geostatistical algorithm and updated according to the ensemble smoother approach, an efficient Bayesian data assimilation technique. The prior distribution includes a spatial correlation function such that the model realizations mimic the geological spatial continuity. The result of the inversion includes a set of realizations of porosity and water saturation, as well as the most likely model and its uncertainty, that are crucial to understand fluid flow, geomechanical, and weathering processes in the critical zone. The proposed approach is validated on two synthetic datasets motivated by the Southern Sierra Critical Zone Observatory and is then applied to data collected on a mountain hillslope near Laramie, Wyoming. The inverted results match the measurements, honor the spatial correlation prior model, and provide geologically realistic petrophysical models of weathered rock at Earth’s surface.

For more information on the published version, visit Springer's Website.  https://link.springer.com/article/10.1007/s11004-022-10006-0


MLA citation style

Li, Ang, et al. Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation In the Critical Zone. . 2022. https://dickinson.hykucommons.org/concern/generic_works/5b0ce931-963a-4de6-9a66-7d87dd550e6b?locale=en

APA citation style

Li, Ang, Carr, Brad J, Callahan, Russell P, Smeltz, Natalie Y, Grana, Dario, Holbrook, W. Steven, Riebe, Clifford S, Singha, Kamini, Hayes, Jorden L, Parsekian, Andrew D, & Flinchum, Brady A. (2022). Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone. https://dickinson.hykucommons.org/concern/generic_works/5b0ce931-963a-4de6-9a66-7d87dd550e6b?locale=en

Chicago citation style

Li, Ang, Carr, Brad J., Callahan, Russell P., Smeltz, Natalie Y., Grana, Dario, Holbrook, W. Steven, Riebe, Clifford S. et al. Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation In the Critical Zone. 2022. https://dickinson.hykucommons.org/concern/generic_works/5b0ce931-963a-4de6-9a66-7d87dd550e6b?locale=en

Note: These citations are programmatically generated and may be incomplete.

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