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Title: Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

Abstract

In this paper, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibrationmore » in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.« less

Authors:
 [1];  [2];  [3];  [2];  [4];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1459653
Alternate Identifier(s):
OSTI ID: 1496976
Report Number(s):
PNNL-SA-116707; SAND-2018-7074J
Journal ID: ISSN 1436-3240; PII: 1571
Grant/Contract Number:  
AC05-76RL01830; AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Stochastic environmental research and risk assessment
Additional Journal Information:
Journal Volume: 32; Journal Issue: 8; Journal ID: ISSN 1436-3240
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Tomographic ground penetrating radar; Soil moisture; Multi-chain Markov chain Monte Carlo; Bayesian

Citation Formats

Bao, Jie, Hou, Zhangshuan, Ray, Jaideep, Huang, Maoyi, Swiler, Laura, and Ren, Huiying. Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework. United States: N. p., 2018. Web. doi:10.1007/s00477-018-1571-8.
Bao, Jie, Hou, Zhangshuan, Ray, Jaideep, Huang, Maoyi, Swiler, Laura, & Ren, Huiying. Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework. United States. doi:10.1007/s00477-018-1571-8.
Bao, Jie, Hou, Zhangshuan, Ray, Jaideep, Huang, Maoyi, Swiler, Laura, and Ren, Huiying. Tue . "Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework". United States. doi:10.1007/s00477-018-1571-8. https://www.osti.gov/servlets/purl/1459653.
@article{osti_1459653,
title = {Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework},
author = {Bao, Jie and Hou, Zhangshuan and Ray, Jaideep and Huang, Maoyi and Swiler, Laura and Ren, Huiying},
abstractNote = {In this paper, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.},
doi = {10.1007/s00477-018-1571-8},
journal = {Stochastic environmental research and risk assessment},
number = 8,
volume = 32,
place = {United States},
year = {2018},
month = {6}
}

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