Modelling of GPR waves for lossy media obeying a complex power law of frequency for dielectric permittivity

The attenuation of ground‐penetrating radar (GPR) energy in the subsurface decreases and shifts the amplitude spectrum of the radar pulse to lower frequencies (absorption) with increasing traveltime and causes also a distortion of wavelet phase (dispersion). The attenuation is often expressed by the quality factor Q. For GPR studies, Q can be estimated from the ratio of the real part to the imaginary part of the dielectric permittivity. We consider a complex power function of frequency for the dielectric permittivity, and show that this dielectric response corresponds to a frequency‐independent‐Q or simply a constant‐Q model. The phase velocity (dispersion relationship) and the absorption coefficient of electromagnetic waves also obey a frequency power law. This approach is easy to use in the frequency domain and the wave propagation can be described by two parameters only, for example Q and the phase velocity at an arbitrary reference frequency. This simplicity makes it practical for any inversion technique. Furthermore, by using the Hilbert transform relating the velocity and the absorption coefficient (which obeys a frequency power law), we find the same dispersion relationship for the phase velocity. Both approaches are valid for a constant value of Q over a restricted frequency‐bandwidth, and are applicable in a material that is assumed to have no instantaneous dielectric response. Many GPR profiles acquired in a dry aeolian environment have shown a strong reflectivity inside dunes. Changes in water content are believed to be the origin of this reflectivity. We model the radar reflections from the bottom of a dry aeolian dune using the 1D wavelet modelling method. We discuss the choice of the reference wavelet in this modelling approach. A trial‐and‐error match of modelled and observed data was performed to estimate the optimum set of parameters characterizing the materials composing the site. Additionally, by combining the complex refractive index method (CRIM) and/or Topp equations for the bulk permittivity (dielectric constant) of moist sandy soils with a frequency power law for the dielectric response, we introduce them into the expression for the reflection coefficient. Using this method, we can estimate the water content and explain its effect on the reflection coefficient and on wavelet modelling.     

Topographic migration of GPR data: Examples from Chad and Mongolia

Most ground-penetrating radar (GPR) measurements are performed on nearly flat areas. If strongly dipping reflections and/or diffractions are present in the GPR data, a classical migration-processing step is needed in order to determine the geometries of shallow structures. Nevertheless, a standard migration routine is not suitable for GPR data collected on areas showing a variable and large topographic relief. To take into account topographic variations, the GPR data are, in general, corrected by applying static shifts instead of using an appropriate topographic migration that would place the reflectors at their correct locations with the right dip angle. In this article, we present an overview of Kirchhoff's migration and show the importance of topographic migration in the case where the depth of the target structures is of the same order as the relief variations. Examples of synthetic and real GPR data are shown to illustrate the efficiency of the topographic migration.     

Ground penetrating radar imaging and time-domain modelling of the infiltration of diesel fuel in a sandbox experiment

Ground penetrating radar (GPR) is a non-destructive method which, over the past 10 years, has been successfully used not only to estimate the water content of soil, but also to detect and monitor the infiltration of pollutants on sites contaminated by light non-aqueous phase liquids (LNAPL). We represented a model water table aquifer (72 cm depth) by injecting water into a sandbox that also contains several buried objects. The GPR measurements were carried out with shielded antennae of 900 and 1200 MHz, respectively, for common mid point (CMP) and constant offset (CO) profiles. We extended the work reported by Loeffler and Bano by injecting 100 L of diesel fuel (LNAPL) from the top of the sandbox. We used the same acquisition procedure and the same profile configuration as before fuel injection. The GPR data acquired on the polluted sand did not show any clear reflections from the plume pollution; nevertheless, travel times are very strongly affected by the presence of the fuel and the main changes are on the velocity anomalies. We can notice that the reflection from the bottom of the sandbox, which is recorded at a constant time when no fuel is present, is deformed by the pollution. The area close to the fuel injection point is characterized by a higher velocity than the area situated further away. The area farther away from the injection point shows a low velocity anomaly which indicates an increase in travel time. It seems that pore water has been replaced by fuel as a result of a lateral flow. We also use finite-difference time-domain (FDTD) numerical GPR modelling in combination with dielectric property mixing models to estimate the volume and the physical characteristics of the contaminated sand.     

Black carbon and chemical characteristics of PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript> at an urban site of North India

The concentrations of PM₁₀, PM_2.5 and their water-soluble ionic species were determined for the samples collected during January to December, 2007 at New Delhi (28.63° N, 77.18° E), India. The annual mean PM₁₀ and PM_2.5 concentrations (± standard deviation) were about 219 (± 84) and 97 (±56) μgm⁻³ respectively, about twice the prescribed Indian National Ambient Air Quality Standards values. The monthly average ratio of PM_2.5/PM₁₀ varied between 0.18 (June) and 0.86 (February) with an annual mean of ∼0.48 (±0.2), suggesting the dominance of coarser in summer and fine size particles in winter. The difference between the concentrations of PM₁₀ and PM_2.5, is deemed as the contribution of the coarse fraction (PM_10−2.5). The analyzed coarse fractions mainly composed of secondary inorganic aerosols species (16.0 μgm⁻³, 13.07%), mineral matter (12.32 μgm⁻³, 10.06%) and salt particles (4.92 μgm⁻³, 4.02%). PM_2.5 are mainly made up of undetermined fractions (39.46 μgm⁻³, 40.9%), secondary inorganic aerosols (26.15 μgm⁻³, 27.1%), salt aerosols (22.48 μgm⁻³, 23.3%) and mineral matter (8.41 μgm⁻³, 8.7%). The black carbon aerosols concentrations measured at a nearby (∼300 m) location to aerosol sampling site, registered an annual mean of ∼14 (±12) μgm⁻³, which is significantly large compared to those observed at other locations in India. The source identifications are made for the ionic species in PM₁₀ and PM_2.5. The results are discussed by way of correlations and factor analyses. The significant correlations of Cl⁻, SO₄²⁻, K⁺, Na⁺, Ca²⁺, NO₃⁻ and Mg²⁺ with PM_2.5 on one hand and Mg²⁺ with PM₁₀ on the other suggest the dominance of anthropogenic and soil origin aerosols in Delhi.     

Reinterpretation of an archaeological pebble culture from the Middle Mekong River valley,Cambodia

In 1963, E. Saurin and J.‐P. Carbonnel discovered the Sre Sbov site on an alluvial terrace of the Mekong River in central Cambodia. Saurin described a lithic typology dating to the Lower/Middle Pleistocene from this site. Although the original lithic assemblage has been lost, this typology has been used continuously as a reference by Southeast Asian prehistorians. In 2007, a Khmer–French team conducted excavations at Sre Sbov that yielded numerous pebbles and cobbles showing apparently convincing handmade removals, as Saurin had previously described. However, an in‐depth study of this assemblage, combined with a geological survey of the area, led to the conclusion that the stones were, in fact, of natural origin, and that for this reason their typology should be disregarded. Using satellite imagery and geological surveys, we explain how such a misinterpretation may have occurred and define a “buffer zone,” corresponding to the maximal extent of the proto‐Mekong River, where fluvially reworked pebbles and cobbles resembling artifacts may be recovered. © 2009 Wiley Periodicals, Inc.     

Predictions of future hydrological conditions and contribution of snow and ice melt in total discharge of Shigar River Basin in Central Karakoram, Pakistan

The high mountains of Hindu-Kush Karakoram and Himalaya(HKKH) contain a large volume of snow and ice, which are the primary sources of water for the entire mountainous population of HKKH. Thus, knowledge of these available resources is very important in relation to their sustainable use. A Modified Positive Degree Day Model was used to simulate daily discharge with the contribution of snow and ice melt from the Shigar River Basin, Central Karakoram, Pakistan. The basin covers an area of 6,921 km2 with an elevation range of 2,204 to 8,611 m a.s.l.. Forty percent of the total area is glaciated among which 20% is covered by debris and remaining 80% by clean ice and permanent snow. To simulate daily discharge, the entire basin was divided into 26 altitude belts. Remotely sensed land cover types are derived by classifying Landsat images of 2009. Daily temperature and precipitation from Skardu meteorological station is used to calibrate the glacio-hydrological model as an input variable after correlating data with the Shigar station data(r=0.88). Local temperature lapse rate of 0.0075 °C/m is used. 2 °C critical temperature is used to separate rain and snow from precipitation. The model is calibrated for 1988~1991 and validated for 1992~1997. The model shows a good Nash-Sutcliffe efficiency and volume difference in calibration(0.86% and 0.90%) and validation(0.78% and 6.85%). Contribution of snow and ice melt in discharge is 32.37% in calibration period and 33.01% is validation period. The model is also used to predict future hydrological regime up to 2099 by using CORDEX South Asia RCM considering RCP4.5 and RCP8.5 climate scenarios.Predicted future snow and ice melt contributions in both RCP4.5 and RCP8.5 are 36% and 37%, respectively. Temperature seems to be more sensitive as compared to other input variables, which is why the contribution of snow and ice in discharge varies significantly throughout the whole century.