Large-scale velocity field and strain tensor in Iran inferred from GPS measurements: new insight for the present-day deformation pattern within NE Iran

A network of 26 GPS sites was implemented in Iran and Northern Oman to measure displacements in this part of the Arabia–Eurasia collision zone. We present the GPS velocity field obtained from three surveys performed in 1999 September, 2001 October and 2005 September and the deduced strain tensor. This study refines previous studies inferred from only the two first surveys. Improvements are significant in NE Iran. The present-day shortening rate across the mountain belts of NE Iran is estimated to 5 ± 1 mm yr⁻¹ at about N11°, 2 ± 1 mm yr⁻¹ of NS shortening across the eastern Kopet Dag and 3 ± 1 mm yr⁻¹ of NS shortening across Binalud and Kuh-e-Sorkh. Our GPS measurements emphasize the varying character of the Kopet Dag deformation between its southeastern part with prevailing thrusting at low rates and its northwestern part with dominant strike-slip activity at increasing rates. The principal axes of the horizontal strain tensor appears very homogeneous from the Zagros to the Alborz and the Kopet-Dag (N20°) and in eastern Iran (Makran and Lut block: N30°). Only NW Iran suffers a variable strain pattern which seems to wrap the Caspian basin. The strain tensor map underlines the existence of large homogeneous tectonic provinces in terms of style and amplitude of the deformation.     

Dependence of theP-wave amplitude spectrum on focal depth

Summary Four spectral parameters, each of which reflects a certain spectral characteristic, are computed for 67 power spectra of earthquakes. The analyzed signals used in this work are theP-phases recorded by short-period, vertical-component seismographs.In correlating the values of the spectral parameters with the respective focal depths, a qualitative spectrum-depth relation is obtained. The most outstanding feature of the relationship thus computed, is a pronounced anomaly in the depth-range of 50–250 km. This is found to correlate rather well with the location of the low-velocity layer which many seismologists have shown to exist in the earth's upper mantle. With the aid of results obtained from experimental work on rock blasts, it is argued that the anomalous zone has more viscous character than the overlying and underlying media.On leave of absence from the Ministry of Oil and Minerals, Baghdad, Iraq     

Estimating extreme losses for the Florida Public Hurricane Model—part II

Theoretical and Applied Climatology - Rising global temperatures are leading to an increase in the number of extreme events and losses ( http://www.epa.gov/climatechange/science/indicators/ )....     

Estimation of daily reference evapotranspiration with limited climatic data using machine learning approaches across different climate zones in New Mexico

Theoretical and Applied Climatology - Reference evapotranspiration (ET0) is a major factor for water resource management. Although the FAO Penman–Monteith model is the highly recommended for...     

UBV photometry and light-curve analysis of Algol

Photoelectric observations of the eclipsing variable β Per, were obtained inUBV standard system, and new elements for the primary minimum were determined as $$J.D. = 2445641.5135,O - C = 0_.^d 0.009.$$ The light curves of the system were analysed using Fourier techniques in the frequency-domain. The fractional radii of both components are $$r_1 = 0.217 \pm 0.002,r_2 = 0.233 \pm 0.002andi = 85.5 \pm 0.5.$$ Absolute elements were derived and the effective temperatures are $$T_1 = 11800K,T_2 = 5140K.$$      

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.