Estimating vadose zone hydraulic properties using ground penetrating radar: The impact of prior information

A number of geophysical methods, such as ground-penetrating radar (GPR), have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, the stochastic inversion of such data within a coupled geophysical-hydrological framework may allow for the effective estimation of vadose zone hydraulic parameters and their corresponding uncertainties. A critical issue in stochastic inversion is choosing prior parameter probability distributions from which potential model configurations are drawn and tested against observed data. A well chosen prior should reflect as honestly as possible the initial state of knowledge regarding the parameters and be neither overly specific nor too conservative. In a Bayesian context, combining the prior with available data yields a posterior state of knowledge about the parameters, which can then be used statistically for predictions and risk assessment. Here we investigate the influence of prior information regarding the van Genuchten-Mualem (VGM) parameters, which describe vadose zone hydraulic properties, on the stochastic inversion of crosshole GPR data collected under steady state, natural-loading conditions. We do this using a Bayesian Markov chain Monte Carlo (MCMC) inversion approach, considering first noninformative uniform prior distributions and then more informative priors derived from soil property databases. For the informative priors, we further explore the effect of including information regarding parameter correlation. Analysis of both synthetic and field data indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when we combine these data with a realistic, informative prior.

Full-waveform inversion of cross-hole ground-penetrating radar data to characterize a gravel aquifer close to the Thur River, Switzerland

Cross-hole radar tomography is a useful tool for mapping shallow subsurface electrical properties viz. dielectric permittivity and electrical conductivity. Common practice is to invert cross-hole radar data with ray-based tomographic algorithms using first arrival traveltimes and first cycle amplitudes. However, the resolution of conventional standard ray-based inversion schemes for cross-hole ground-penetrating radar (GPR) is limited because only a fraction of the information contained in the radar data is used. The resolution can be improved significantly by using a full-waveform inversion that considers the entire waveform, or significant parts thereof. A recently developed 2D time-domain vectorial full-waveform crosshole radar inversion code has been modified in the present study by allowing optimized acquisition setups that reduce the acquisition time and computational costs significantly. This is achieved by minimizing the number of transmitter points and maximizing the number of receiver positions. The improved algorithm was employed to invert cross-hole GPR data acquired within a gravel aquifer (4-10 m depth) in the Thur valley, Switzerland. The simulated traces of the final model obtained by the full-waveform inversion fit the observed traces very well in the lower part of the section and reasonably well in the upper part of the section. Compared to the ray-based inversion, the results from the full-waveform inversion show significantly higher resolution images. At either side, 2.5 m distance away from the cross-hole plane, borehole logs were acquired. There is a good correspondence between the conductivity tomograms and the natural gamma logs at the boundary of the gravel layer and the underlying lacustrine clay deposits. Using existing petrophysical models, the inversion results and neutron-neutron logs are converted to porosity. Without any additional calibration, the values obtained for the converted neutron-neutron logs and permittivity results are very close and similar vertical variations can be observed. The full-waveform inversion provides in both cases additional information about the subsurface. Due to the presence of the water table and associated refracted/reflected waves, the upper traces are not well fitted and the upper 2 m in the permittivity and conductivity tomograms are not reliably reconstructed because the unsaturated zone is not incorporated into the inversion domain.

Deletion of mu- and kappa-opioid receptors in mice changes epidermal hypertrophy, density of peripheral nerve endings, and itch behavior.

The mu- (MOR) and kappa- (KOR) opioid receptors have been implicated in the regulation of homeostasis of non-neuronal cells, such as keratinocytes, and sensations like pain and chronic pruritus. Therefore, we have studied the phenotype of skin after deletion of MOR and KOR. In addition, we applied a dry skin model in these knockout mice and compared the different mice before and after induction of the dermatitis in terms of epidermal thickness, epidermal peripheral nerve ending distribution, dermal inflammatory infiltrate (mast cells, CD4 positive lymphocytes), and scratching behavior. MOR knockout mice reveal as phenotype a significantly thinner epidermis and a higher density of epidermal fiber staining by protein gene product 9.5 than the wild-type counterparts. Epidermal hypertrophy, induced by the dry skin dermatitis, was significantly less developed in MOR knockout than in wild-type mice. Neither mast cells nor CD4 T(h)-lymphocytes are involved in the changes of epidermal nerve endings and epidermal homeostasis. Finally, behavior experiments revealed that MOR and KOR knockout mice scratch less after induction of dry skin dermatitis than wild-type mice. These results indicate that MOR and KOR are important in skin homeostasis, epidermal nerve fiber regulation, and pathophysiology of itching.

Membrane mu poly(A) signal and 3' flanking sequences function as a transcription terminator for immunoglobulin-encoding genes.

Developmentally regulated mechanisms involving alternative RNA splicing and/or polyadenylation, as well as transcription termination, are implicated in controlling the levels of secreted mu (mu s), membrane mu (mu m) and delta immunoglobulin (Ig) heavy chain mRNAs during B cell differentiation (mu gene encodes the mu heavy chain). Using expression vectors constructed with genomic DNA segments composed of the mu m polyadenylation signal region, we analyzed poly(A) site utilization and termination of transcription in stably transfected myeloma cells and in murine fibroblast L cells. We found that the gene segment containing the mu m poly(A) signals, along with 536 bp of downstream flanking sequence, acted as a transcription terminator in both myeloma cells and L cell fibroblasts. Neither a 141-bp DNA fragment (which directed efficient polyadenylation at the mu m site), nor the 536-bp flanking nucleotide sequence alone, were sufficient to obtain a similar regulation. This shows that the mu m poly(A) region plays a central role in controlling developmentally regulated transcription termination by blocking downstream delta gene expression. Because this gene segment exhibited the same RNA processing and termination activities in fibroblasts, it appears that these processes are not tissue-specific.

Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics.