Phases greater than 90° in MT data: Analysis using dimensionality tools

An example of magnetotelluric data is discussed which has distinctive phase values out of quadrant, and for which the Z_xx element of the measured tensor is greater than the Z_xy element in both real and quadrature parts. Mohr circle plots and principal value decompositions of the real and quadrature parts of the tensor clarify its understanding. An analysis of rotational invariants of the data suggests a case of galvanic distortion of a 2D structure. Results from phase tensor analysis are included, and the example is seen to be a dramatic instance of phase tensor analysis reducing an apparently 3D example to 2D characteristics.The Mohr circles of this example do not capture the axes origin, although there are phases out of quadrant. Extra intrigue is thus added to the question, noted in Lilley, F.E.M., 1998. Magnetotelluric tensor decomposition: part I, theory for a basic procedure. Geophysics 63, 1885–1897 and arising independently in Caldwell, T.G., Bibby, H. M., Brown, C., 2004. The magnetotelluric phase tensor. Geophys. J. Int. 158, 457–469 and Bibby, H.M., Caldwell, T.G., Brown, C., 2005. Determinable and non-determinable parameters of galvanic distortion in magnetotellurics. Geophys. J. Int. 163, 915–930, whether the determinant values taken separately of the real and quadrature parts of a magnetotelluric tensor should both never be negative.For data whose Mohr circles do not capture the axes origin, simple conditions are derived regarding phase. These conditions govern whether or not it is formally possible for observed phases of Z_xy to exceed 90°, for any rotation of the observing axes.     

Climate change and future flows of Rocky Mountain rivers: converging forecasts from empirical trend projection and down‐scaled global circulation modelling

To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non‐parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960–1989 data, we assessed climate forecasts from six GCMs: CGCM1‐A, HadCM3, NCAR‐CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R² about 0·7), and averaging over three decades closely reconstructed the monthly pattern (R² = 0·94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (?3%, 2005–2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (?9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about ? 15%) and annual flows (about ? 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies. Copyright © 2010 John Wiley & Sons, Ltd.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.     

The effect of the atmosphere on the propagation of high-power ultrabroadband microwave emission pulses

The effect of the atmosphere on the propagation of single successive pulses of the ultrabroadband microwave emission has been calculated in detail. It has been indicated that nonlinear effects mainly influence the propagation of the LF pulse part in the absence of divergence, which substantially affects the propagation of the following pulses.     

Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective,laboratory and in situ estimates

Simulation of soil moisture content requires effective soil hydraulic parameters that are valid at the modelling scale. This study investigates how these parameters can be estimated by inverse modelling using soil moisture measurements at 25 locations at three different depths (at the surface, at 30 and 60 cm depth) on an 80 by 20 m hillslope. The study presents two global sensitivity analyses to investigate the sensitivity in simulated soil moisture content of the different hydraulic parameters used in a one‐dimensional unsaturated zone model based on Richards' equation. For estimation of the effective parameters the shuffled complex evolution algorithm is applied. These estimated parameters are compared to their measured laboratory and in situ equivalents. Soil hydraulic functions were estimated in the laboratory on 100 cm³ undisturbed soil cores collected at 115 locations situated in two horizons in three profile pits along the hillslope. Furthermore, in situ field saturated hydraulic conductivity was estimated at 120 locations using single‐ring pressure infiltrometer measurements. The sensitivity analysis of 13 soil physical parameters (saturated hydraulic conductivity (K_s), saturated moisture content (θ_s), residual moisture content (θ_r), inverse of the air‐entry value (α), van Genuchten shape parameter (n), Averjanov shape parameter (N) for both horizons, and depth (d) from surface to B horizon) in a two‐layer single column model showed that the parameter N is the least sensitive parameter. K_s of both horizons, θ_s of the A horizon and d were found to be the most sensitive parameters. Distributions over all locations of the effective parameters and the distributions of the estimated soil physical parameters from the undisturbed soil samples and the single‐ring pressure infiltrometer estimates were found significantly different at a 5% level for all parameters except for α of the A horizon and K_s and θ_s of the B horizon. Different reasons are discussed to explain these large differences. Copyright © 2004 John Wiley & Sons, Ltd.     

Ophiolitic association of Cape Fiolent area,southwestern Crimea

An ophiolitic association consisting of serpentinized ultramafic rocks and serpentinite, layered mafic–ultramafic complex, gabbro and gabbrodolerite, fragments of parallel dike complex, pillow lava, black bedded chert, and jasper has been identified for the first time by authors in the Cape Fiolent area. The chemistry of pillow lavas and dolerites, including REE patterns and a wide set of other microelements, indicates suprasubduction nature of the ophiolites and their belonging to a backarc basin that has reached the stage of spreading in its evolution.