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Recovering material properties of the subsurface using ground-penetrating radar (GPR) data of finite bandwidth with missing low frequencies and in the presence of strong attenuation is a challenging problem. We have adopted three nonlinear inverse methods for recovering electrical conductivity and permittivity of the subsurface by joining GPR multioffset and electrical resistivity (ER) data acquired at the surface. All of the methods use ER data to constrain the low spatial frequency of the conductivity solution. The first method uses the envelope of the GPR data to exploit low-frequency content in full-waveform inversion and does not assume structural similarities of the material properties. The second method uses cross gradients to manage weak amplitudes in the GPR data by assuming structural similarities between permittivity and conductivity. The third method uses the envelope of the GPR data and the cross gradient of the model parameters. By joining ER and GPR data, exploiting low-frequency content in the GPR data, and assuming structural similarities between the electrical permittivity and conductivity, we are able to recover subsurface parameters in regions where the GPR data have a signal-to-noise ratio close to one.
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Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data
In a previous work, the first two authors proposed a data-driven method that can construct a site specific multivariate probability density function model for soil properties using sparse, incomplete, and spatially variable site investigation data. The spatial variability was limited to the depth direction (horizontal variability was not considered). This data-driven method is referred to as GPR-MUSIC-X. In the current paper, two improvements with respect to GPRMUSIC-X are made. First, the one-dimensional spatial variability considered by GPR-MUSIC-X is extended to three-dimensional spatial variability (denoted by GPR-MUSIC-3X). Second, a hierarchical Bayesian model (HBM) is adopted to learn the cross-correlation (correlation among different soil parameters) behaviour of generic sites in a soil database accounting for site differences (or uniqueness), and the learning outcome is incorporated into GPR-MUSIC-3X. The resulting model is a quasi-site-specific model (denoted by HBM-MUSIC-3X) because it not only is based on site-specific data but also is informed by the soil database in a manner sensitive to site uniqueness. A case history is used to illustrate the effectiveness of the proposed HBM MUSIC-3X.
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- Award ID(s):
- 1931069
- PAR ID:
- 10309343
- Date Published:
- Journal Name:
- Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
- ISSN:
- 1749-9518
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/17499518.2021.1971256
