HYDROGEOPHYSICSCross-well ground penetrating radar (GPR) data sets were collected in the vadose zone of an ice-contact delta near Oslo's Gardermoen Airport (Norway) before, during, and after snowmelt in 2005. The observed travel times were inverted using curved-ray travel time tomography. The tomograms are in good agreement with the local geologic structure of the delta. The tomographic results were confi rmed independently by surface GPR refl ection data and x-ray images of core samples. In addition to structure, the GPR tomograms also show a strong time dependency due to the snowmelt. The time-lapse tomograms were used to estimate volumetric soil water content using Topp's equation. The volumetric soil water content was also observed independently by using a neutron meter. Comparison of these two methods revealed a strong irregular wetting process during the snowmelt. This was interpreted to be due to soil heterogeneity as well as a heterogeneous infi ltration rate. The geologic structure and water content estimates obtained from the GPR tomography can be used in forward and inverse fl ow modeling. Finally, the water balance in the vadose zone was calculated using snow accumulation data, precipitation data, porosity estimates, and observed changes in the groundwater table. The amount of water stored in the vadose zone obtained from the water balance is consistent with the amount estimated using GPR tomography. Alternatively, the change in water storage in the vadose zone can be estimated using GPR tomography. This may then permit estimates of evapotranspiration to be made, provided other components of the water balance are known.
1] A method is presented to estimate flow parameters and geological structure in the vadose zone by combining time-lapse Ground Penetrating Radar (GPR) traveltime tomography and inverse flow modeling. The traveltime tomography is used to determine the spatial electromagnetic velocity distribution in the vadose zone. These time-lapse velocity images are converted to time-lapse volumetric soil water content images using petrophysical relationships. Subsequently, the water content images are used as constraints in the flow inversion. The influence of the tomographic artifacts on the flow inversion is minimized by assigning weights that are proportional to the ray coverage. Our flow inversion algorithm estimates the flow parameters and calibrates the geological structure. The geological structure is defined using a set of control points, the positions of which can be modified during the inversion. After the inversion, the final geological and flow model are used to compute GPR traveltimes to check the consistency between these computed traveltimes and the observed traveltimes.The method is first tested on two synthetic models (a steady state and a transient flow models). Subsequently, the method is applied to characterize a real vadose zone at Oslo Airport Gardermoen, Norway, during the snowmelt in 2005. The flow inversion method is applied to locate and quantify the main geological layers at the site. In particular the inversion method identifies and estimates the location and properties of thin dipping layers with relatively lowpermeability. The flow model is cross validated using an independent infiltration event. Citation: Farmani, M. B., N.-O. Kitterød, and H. Keers (2008), Inverse modeling of unsaturated flow parameters using dynamic geological structure conditioned by GPR tomography, Water Resour. Res., 44, W08401,
We present a new approach to generating 3D location maps for microseismic events from 3-component data. The method combines full-waveform vector migration with an imaging condition based on semblance-weighted deconvolution. The semblance-weighted deconvolution keys in on the signal-to-noise conditions of the data to give a high-resolution, low-noise estimate of the locations for the microseismic sources. As the method requires no explicit time picking or event association, it is well suited for an either fully-automated or a semi-automated process. Almost as a by-product, the method provides a natural measure of the uncertainty associated with the locations of the individual micro-earthquakes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.