Geostatistical inference using crosshole ground-penetrating radar

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Standard

Geostatistical inference using crosshole ground-penetrating radar. / Looms, Majken Caroline; Hansen, Thomas Mejer; Cordua, Knud S.; Nielsen, Lars; Jensen, Karsten Høgh; Binley, Andrew.

In: Geophysics, Vol. 75, No. 6, 2010, p. J29-J41.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Looms, MC, Hansen, TM, Cordua, KS, Nielsen, L, Jensen, KH & Binley, A 2010, 'Geostatistical inference using crosshole ground-penetrating radar', Geophysics, vol. 75, no. 6, pp. J29-J41. https://doi.org/10.1190/1.3496001

APA

Looms, M. C., Hansen, T. M., Cordua, K. S., Nielsen, L., Jensen, K. H., & Binley, A. (2010). Geostatistical inference using crosshole ground-penetrating radar. Geophysics, 75(6), J29-J41. https://doi.org/10.1190/1.3496001

Vancouver

Looms MC, Hansen TM, Cordua KS, Nielsen L, Jensen KH, Binley A. Geostatistical inference using crosshole ground-penetrating radar. Geophysics. 2010;75(6):J29-J41. https://doi.org/10.1190/1.3496001

Author

Looms, Majken Caroline ; Hansen, Thomas Mejer ; Cordua, Knud S. ; Nielsen, Lars ; Jensen, Karsten Høgh ; Binley, Andrew. / Geostatistical inference using crosshole ground-penetrating radar. In: Geophysics. 2010 ; Vol. 75, No. 6. pp. J29-J41.

Bibtex

@article{af7f663e62cb409fa2d16e645bcf0574,
title = "Geostatistical inference using crosshole ground-penetrating radar",
abstract = "High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state variables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole ground-penetrating-radar (GPR) traveltimes result in smooth, minimum-variance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance andmean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately 10 m apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisture-content measurements, obtained gravimetrically from samples collected at the field site.",
author = "Looms, {Majken Caroline} and Hansen, {Thomas Mejer} and Cordua, {Knud S.} and Lars Nielsen and Jensen, {Karsten H{\o}gh} and Andrew Binley",
year = "2010",
doi = "10.1190/1.3496001",
language = "English",
volume = "75",
pages = "J29--J41",
journal = "Geophysics",
issn = "0016-8033",
publisher = "Society of Exploration Geophysicists",
number = "6",

}

RIS

TY - JOUR

T1 - Geostatistical inference using crosshole ground-penetrating radar

AU - Looms, Majken Caroline

AU - Hansen, Thomas Mejer

AU - Cordua, Knud S.

AU - Nielsen, Lars

AU - Jensen, Karsten Høgh

AU - Binley, Andrew

PY - 2010

Y1 - 2010

N2 - High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state variables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole ground-penetrating-radar (GPR) traveltimes result in smooth, minimum-variance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance andmean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately 10 m apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisture-content measurements, obtained gravimetrically from samples collected at the field site.

AB - High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state variables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole ground-penetrating-radar (GPR) traveltimes result in smooth, minimum-variance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance andmean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately 10 m apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisture-content measurements, obtained gravimetrically from samples collected at the field site.

U2 - 10.1190/1.3496001

DO - 10.1190/1.3496001

M3 - Journal article

VL - 75

SP - J29-J41

JO - Geophysics

JF - Geophysics

SN - 0016-8033

IS - 6

ER -

ID: 32398234