Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

Bringing GPS into harsh environments for fully automated deformation monitoring

Engineering projects that require deformation monitoring frequently utilize geodetic sensors to measure displacements of target points located in the deformation zone. In situations where control stations and targets are separated by a kilometer or more, GPS can offer higher precision position updates at more frequent intervals than can normally be achieved using total station technology. For large-scale deformation projects requiring the highest precision, it is therefore advisable to use a combination of the two sensors. In response to the need for high precision, continuous GPS position updates in harsh deformation monitoring environments, a software has been developed that employs triple-differenced carrier-phase measurements in a delayed-state Kalman filter. Two data sets were analyzed to test the capabilities of the software. In the first test, a GPS antenna was displaced using a translation stage to mimic slow deformation. In the second test, data collected at a large open pit mine were processed. It was shown that the delayed-state Kalman filter developed could detect millimeter-level displacements of a GPS antenna. The actual precision attained depends upon the amount of process noise infused at each epoch to accommodate the antenna displacements. Higher process noise values result in quicker detection times, but at the same time increase the noise in the solutions. A slow, 25 mm displacement was detected within 30 min of the full displacement with sigma values in E, N and U of ±10 mm or better. The same displacement could also be detected in less than 5 h with sigma values in E, N and U of ±5 mm or better. The software works best for detecting long period deformations (e.g., 20 mm per day or less) for which sigma values of 1–2 mm are attained in all three solution components. It was also shown that the triple-differenced carrier-phase observation can be used to significantly reduce the effects of residual tropospheric delay that would normally plague double-differenced observations in harsh GPS environments.

An approach to aid INS using time-differenced GPS carrier phase (TDCP) measurements

This paper presents a simple and effective approach that incorporates single-frequency, L1 time-differenced GPS carrier phase (TDCP) measurements without the need of ambiguity resolution techniques and the complexity to accommodate the delayed-state terms. Static trial results are included to illustrate the stochastic characteristics and effectiveness of the TDCP measurements in controlling position error growth. The formulation of the TDCP observation model is also described in a 17-state tightly-coupled GPS/INS iterative, extended Kalman filter (IEKF) approach. Preliminary land vehicle trial results are also presented to illustrate the effectiveness of the TDCP which provides sub-meter positional accuracies when operating for more than 10 min.     

GPS signal acquisition using the repeatability of successive code phase measurements

This paper introduces a new method for GPS signal acquisition, which is based on the repeatability of successive code phase measurements and the M-of-N search algorithm. The performance of the proposed method in terms of probability of signal detection is similar to that of traditional methods, except that the calculation of the probability of detection does not rely on the noise distribution or the Carrier-to-Noise ratio (C/N₀). The code phase repeatability-based method is presented along with equations for probability of detection and probability of false detection. If the distribution of the noise is known, it also provides an estimate of the C/N₀. The proposed method is illustrated for coherent and non-coherent acquisition and C/N₀ estimation.     

Cross-scale analysis of cluster correspondence using different operational neighborhoods

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.      

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.

Mirdita Zone ophiolites and associated sediments in Albania reveal Neotethys Ocean origin

The Mirdita Ophiolite Zone in Albania is associated with widespread mélanges containing components of up to nappe-size. We dated matrix and components of the mélange by radiolarians, conodonts, and other taxa. The components consist of radiolarites, pelagic limestones and shallow-water limestones, all of Triassic age, as well as ophiolites. Triassic radiolarite as a primary cover of ophiolite material proves Middle Triassic onset of Mirdita ocean-floor formation. The mélange contains a turbiditic radiolarite-rich matrix (“radiolaritic flysch”), dated as Late Bajocian to Early Oxfordian. It formed as a synorogenic sediment during west-directed thrusting of ophiolite and sediment-cover nappes representing ocean floor and underplated fragments of the western continental margin. The tectonic structures formed during these orogenic events (“Younger Kimmeridian or Eohellenic Orogeny”) are sealed by Late Jurassic platform carbonates. The geological history conforms with that of the Inner Dinarides and adjoining areas; we therefore correlate the Mirdita-Pindos Ophiolite Zone with the Vardar Zone and explain its present position by far-distance west-directed thrusting.     

Light Requirements for Growth and Survival of Eelgrass (<Emphasis Type="Italic">Zostera marina</Emphasis> L.) in Pacific Northwest (USA) Estuaries

We developed light requirements for eelgrass in the Pacific Northwest, USA, to evaluate the effects of short- and long-term reductions in irradiance reaching eelgrass, especially related to turbidity and overwater structures. Photosynthesis-irradiance experiments and depth distribution field studies indicated that eelgrass productivity was maximum at a photosynthetic photon flux density (PPFD) of about 350–550 μmol quanta m⁻² s⁻¹. Winter plants had approximately threefold greater net apparent primary productivity rate at the same irradiance as summer plants. Growth studies using artificial shading as well as field monitoring of light and eelgrass growth indicated that long-term survival required at least 3 mol quanta m⁻² day⁻¹ on average during spring and summer (i.e., May-September), and that growth was saturated above about 7 mol quanta m⁻² day⁻¹. We conclude that non-light-limited growth of eelgrass in the Pacific Northwest requires an average of at least 7 mol quanta m⁻² day⁻¹ during spring and summer and that long-term survival requires a minimum average of 3 mol quanta m⁻² day⁻¹.     

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

This paper proposes a novel history-matching method where reservoir structure is inverted from dynamic fluid flow response. The proposed workflow consists of searching for models that match production history from a large set of prior structural model realizations. This prior set represents the reservoir structural uncertainty because of interpretation uncertainty on seismic sections. To make such a search effective, we introduce a parameter space defined with a “similarity distance” for accommodating this large set of realizations. The inverse solutions are found using a stochastic search method. Realistic reservoir examples are presented to prove the applicability of the proposed method.