Abstract

The warming climate in high-latitude permafrost regions is leading to permafrost degradation. Estimating seismic wave velocities in permafrost could help predict the geomechanical properties of permafrost and provide information to plan and design resilient civil infrastructure in cold regions. This paper evaluates the performance of seven models when predicting the seismic wave velocities of permafrost statistically; these models are the time-average, Zimmerman and King, Minshull et al., weighted equation, three-phase, Biot–Gassmann theory modified by Lee (BGTL), and Dou et al. models. The data used in the evaluation are from published laboratory and in situ data, which includes 369 data points for joint P and S wave velocities from nine publications and 943 unfrozen water content data points from 12 publications. The unfrozen water content that is used in these models is determined from a modified Dall’Amico’s model that is proposed, which is evaluated against six existing unfrozen water content models based on soil temperature. This paper finds that saturated nonsaline permafrost generally shares similar linear trends between the P and S wave velocities, regardless of soil type, porosity, grain size, and temperature. Fitting all existing data, an empirical linear relationship is derived between the P and S wave velocities. Among the seven models evaluated, the Minshull et al. and BGTL models are the most accurate when predicting the seismic velocities of permafrost.

Practical Applications

Unfrozen water content and seismic wave velocity models are valuable tools for quantitatively predicting permafrost dynamics and degradation, with practical applications in various engineering areas with permafrost environments. As permafrost thaws due to rising temperatures, these models could be used to guide the quantitative interpretation of geophysical changes in subsurface conditions, assess the potential for ground instability, and predict future permafrost degradation. Unfrozen water content models are used to predict the percentage of unfrozen water within permafrost, which links the changes with permafrost temperature. Unfrozen water content models of permafrost are essential when assessing permafrost thaw, thermal performance, heat transfer processes in permafrost, and the effect of civil infrastructure on permafrost (Chen et al.,). The seismic wave velocity models could help engineers assess the subsurface conditions in permafrost areas; this assessment is crucial for environmental and seismic monitoring, land use planning, infrastructure design and construction, and natural resources exploration.

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Data Availability Statement

Some or all data, models, or codes generated or used during the study are available in a repository or online in accordance with funder data retention policies. Ji, X., Liew, M., and Xiao, M. 2022. Supplementary data for “Statistical Evaluation of Seismic Wave Velocity Models of Permafrost.” Penn State Data Commons. https://doi.org/10.26208/6H4X-JC81.

Acknowledgments

This study is supported by the National Science Foundation under Grants CMMI-2034363, CMMI-2034366, and ICER-1927718. Dr. Min Liew provided advice on data collection.
Author contributions: Xiaohang Ji: Conceptualization, methodology, software, formal analysis, investigation, writing the original draft, and visualization. Ming Xiao: Conceptualization, methodology, validation, writing (review and editing), visualization, supervision, project administration, and funding acquisition. Eileen Martin: Conceptualization, writing (review and editing), project administration, and funding acquisition. Tieyuan Zhu: Conceptualization, writing (review and editing), project administration, and funding acquisition.

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Journal of Cold Regions Engineering
Volume 38Issue 3September 2024

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Received: Jun 22, 2023
Accepted: Jan 10, 2024
Published online: Jun 11, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 11, 2024

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Graduate Student, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, PA 16802 (corresponding author). ORCID: https://orcid.org/0000-0002-1607-662X. Email: [email protected]
Ming Xiao, F.ASCE [email protected]
Professor, Dept. of Civil and Environmental Engineering, Pennsylvania State Univ., University Park, PA 16802. Email: [email protected]
Assistant Professor, Dept. of Geophysics and Applied Math and Statistics, Colorado School of Mines, Golden, CO 80401. ORCID: https://orcid.org/0000-0002-3420-4971. Email: [email protected]
Tieyuan Zhu [email protected]
Associate Professor, Dept. of Geosciences, Pennsylvania State Univ., University Park, PA 16802. Email: [email protected]

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