TY - JOUR

T1 - Accounting for Modeling Errors in Linear Inversion of Crosshole Ground-Penetrating Radar Amplitude Data

T2 - Detecting Sand in Clayey Till

AU - Jensen, B. B.

AU - Hansen, T. M.

AU - Cordua, K. S.

AU - Tuxen, N.

AU - Tsitonaki, A.

AU - Looms, M. C.

N1 - Publisher Copyright: © 2022. The Authors.

PY - 2022

Y1 - 2022

N2 - Mapping high permeability sand occurrences in clayey till is fundamental for protecting the underlying drinking water resources. Crosshole ground penetrating radar (GPR) amplitude data have the potential to differentiate between sand and clay, and can provide 2D subsurface models with a decimeter-scale resolution. We develop a probabilistic straight-ray-based inversion scheme, where we account for the forward modeling error arising from choosing a straight-ray forward solver. The forward modeling error is described by a Gaussian probability distribution and included in the total noise model by addition of covariance models. Due to the linear formulation, we are able to decouple the inversion of traveltime and amplitude data and obtain results fast. We evaluate the approach through a synthetic study, where synthetic traveltime and amplitude data are inverted to obtain slowness and attenuation tomograms using several noise model scenarios. We find that accounting for the forward modeling error is fundamental to successfully obtain tomograms without artifacts. This is especially the case for inversion of amplitude data since the structure of the noise model for the forward modeling error is significantly different from the other data error models. Overall, inversion of field data confirms the results from the synthetic study; however, amplitude inversion performs slightly better than traveltime inversion. We are able to characterize a 0.4–0.6 m thick sand layer as well as internal variations in the clayey till matching observed geological information from borehole logs and excavation.

AB - Mapping high permeability sand occurrences in clayey till is fundamental for protecting the underlying drinking water resources. Crosshole ground penetrating radar (GPR) amplitude data have the potential to differentiate between sand and clay, and can provide 2D subsurface models with a decimeter-scale resolution. We develop a probabilistic straight-ray-based inversion scheme, where we account for the forward modeling error arising from choosing a straight-ray forward solver. The forward modeling error is described by a Gaussian probability distribution and included in the total noise model by addition of covariance models. Due to the linear formulation, we are able to decouple the inversion of traveltime and amplitude data and obtain results fast. We evaluate the approach through a synthetic study, where synthetic traveltime and amplitude data are inverted to obtain slowness and attenuation tomograms using several noise model scenarios. We find that accounting for the forward modeling error is fundamental to successfully obtain tomograms without artifacts. This is especially the case for inversion of amplitude data since the structure of the noise model for the forward modeling error is significantly different from the other data error models. Overall, inversion of field data confirms the results from the synthetic study; however, amplitude inversion performs slightly better than traveltime inversion. We are able to characterize a 0.4–0.6 m thick sand layer as well as internal variations in the clayey till matching observed geological information from borehole logs and excavation.

KW - attenuation

KW - crosshole methods

KW - ground penetrating radar

KW - hydrogeophysics

KW - model error

KW - tomography

U2 - 10.1029/2022JB024666

DO - 10.1029/2022JB024666

M3 - Journal article

AN - SCOPUS:85141655418

VL - 127

JO - Journal of Geophysical Research: Solid Earth

JF - Journal of Geophysical Research: Solid Earth

SN - 0148-0227

IS - 10

M1 - e2022JB024666

ER -