Directional spread parameter at intermediate water depth

The characteristics of directional spread parameters at intermediate water depth are investigated based on a cosine power ‘2s' directional spreading model. This is based on wave measurements carried out using a Datawell directional waverider buoy in 23 m water depth. An empirical equation for the frequency dependent directional spreading parameter is presented. Directional spreading function estimated based on the Maximum Entropy Method is compared with those obtained using a cosine power ‘2s' parameter model. A set of empirical equations relating the directional spreading parameter corresponding to the peak of wave spectrum to other wave parameters like significant wave height and period are obtained. It shows that the wave directional spreading at peak wave frequency can be related to the non-linearity parameter, which allows estimation of directional spreading without reference to wind information.     

Interpolation of wave heights

Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output.     

Preliminary magnetic susceptibility characteristics of the Bharati promontory (Grovness Peninsula), Larsemann Hills,Prydz Bay region,East Antarctica

The coastal tract of the Prydz Bay region in the East Antarctica exposes Archean to Late Proterozoic magmatic and medium- to high grade (amphibolite — granulite facies) metamorphic rocks. The para- and ortho gneisses from the Bharati promontory (Grovness Peninsula) forming a part of the Larsemann Hills in the southern segment of Prydz Bay were investigated for magnetic characterization. In this small peninsula the upper amphibolite facies gneisses occur as NE-trending bands. The para-gneisses show a range of mineral assemblages (± cordierite ± sillimanite ±garnet) while ortho-gneiss mineralogy includes quartz, feldspar, biotite, garnet. All the lithological units in Bharati promontory contain ubiquitous magnetite, however, with wide variation in the volume proportions. This has resulted in a wide range in magnetic susceptibility (10^?4 to 10^?2 SI). Magnetic foliations show a correspondence with the general trend of lithounits (050° NE) and define a resulting geometry of mainly D₁ and D₂ foliations. The magnetic lineations show a preferred orientation with moderate easterly plunge (mean vector 093/36). The findings have implications for the magnetic field survey because such fabrics would impart a strong horizontal component of induced magnetization.     

Artificial Neural Network as a Tool for Backbreak Prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R² = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively.     

Age of the Marwar Supergroup,NW India:A note on the U–Pb geochronology of Jodhpur Group felsic volcanics

The Marwar Supergroup(NW Peninsular India)is thought to be of Ediacaran-Cambrian age,based on previous paleontological and geochronological studies.However,direct constraints on the onset of sedimentation within the Marwar basin are still scarce.In this study,we report U–Pb zircon,LA-ICP-MS,and SIMS ages from the Chhoti Khatu felsic volcanic rocks,interlayered with the Jodhpur Group sandstones(Lower Marwar Supergroup).The cathodoluminescence images of the zircons indicate complex morphologies,and core-rim textures coupled with the wide range of ages indicate that they are likely inherited or in the case of thin poorly indurated ash-beds,detrital in origin.The age spectra of 68 zircon analyses from our sampling display a dominant 800–900 Ma age peak corresponding to the age of basement"Erinpura granite"rocks in the region.The youngest inherited zircon from a felsic ash layer yielded a U–Pb age of651 Ma±18 Ma that,together with previous studies and paleontological evidence,indicates a postCryogenian age for the initiation of Marwar sedimentation following a~125 Ma hiatus between the end of Malani magmatism and Marwar deposition.     

Changes in climatic elements in the Pan-Hexi region during 1960–2014 and responses to global climatic changes

Theoretical and Applied Climatology - In this paper, robust statistical methods (including the climatic tendency ratio, inverse distance weight (IDW), and Mann-Kendall’s non-parametric...     

Crop classification using biologically-inspired techniques with high resolution satellite image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.     

Maintaining real-time precise point positioning during outages of orbit and clock corrections

The precise point positioning (PPP) is a popular positioning technique that is dependent on the use of precise orbits and clock corrections. One serious problem for real-time PPP applications such as natural hazard early warning systems and hydrographic surveying is when a sudden communication break takes place resulting in a discontinuity in receiving these orbit and clock corrections for a period that may extend from a few minutes to hours. A method is presented to maintain real-time PPP with 3D accuracy less than a decimeter when such a break takes place. We focus on the open-access International GNSS Service (IGS) real-time service (RTS) products and propose predicting the precise orbit and clock corrections as time series. For a short corrections outage of a few minutes, we predict the IGS-RTS orbits using a high-order polynomial, and for longer outages up to 3 h, the most recent IGS ultra-rapid orbits are used. The IGS-RTS clock corrections are predicted using a second-order polynomial and sinusoidal terms. The model parameters are estimated sequentially using a sliding time window such that they are available when needed. The prediction model of the clock correction is built based on the analysis of their properties, including their temporal behavior and stability. Evaluation of the proposed method in static and kinematic testing shows that positioning precision of less than 10 cm can be maintained for up to 2 h after the break. When PPP re-initialization is needed during the break, the solution convergence time increases; however, positioning precision remains less than a decimeter after convergence.     

Numerical Study of Mine Site Specific Multiseam Mining and Its Impact on Surface Subsidence and Chain Pillar Stress

This paper investigates various multiseam mining related parameters using mine site specific data and numerical simulations. Two important mining effects—subsidence and stress—are analysed for different possible mining layouts. A geological mine dataset has been used to generate a numerical model. The predicted surface subsidence magnitude and surface profile have been compared under different scenarios to assess potential options in multiseam mining strategies. The effects that seam separation distances, mining offset, panel layout and panel orientation each have on surface subsidence and chain pillar stress magnitude have been investigated. The numerical simulation shows that ascending or descending mining directions have little impact on controlling the surface subsidence in multiseam mining and predicted an almost identical maximum stress development at the chain pillars. Numerical simulations infer that the orientation of the top panels control the subsidence profile.