Application of visual,statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait

The ability of artificial neural network to differentiate water samples from the two aquifers of Kuwait on the basis of their major ion chemistry has been demonstrated. The major ion concentration distribution in the groundwater of the Kuwait Group and the Dammam Formation aquifers of Kuwait appears very similar. Cross-plots, supported by the discriminant function analysis of the data, however, suggest that there are some subtle differences in the overall composition of the water from the two aquifers that make it possible to differentiate the water from the two aquifers in almost 80% of the cases. An artificial neural network improved the differentiation capability to 90% of the cases. It is also possible to estimate the fraction of Kuwait Group water in the flow stream of dually completed wells with the help of an artificial neural network developed for this purpose. Electronic Publication     

A coupled,dynamical and chemical model for the prestellar core L1544: Comparison of modeled and observed C<Superscript>18</Superscript>O,HCO<Superscript>+</Superscript>, and CS emission spectra

We consider radiative transfer in C¹⁸O, HCO⁺, and CS molecular lines in a spherically symmetrical, coupled, dynamical and chemical model of a prestellar core whose evolution is determined by ambipolar diffusion. Theoretical and observed line profiles are compared for the well-studied core L1544, which may be a collapsing protostellar cloud. We study the relationship between the line shapes and model parameters. The structure of the envelope and kinematic parameters of the cloud are the most important factors determining the shape of the lines. Varying the input model parameters for the radiative transfer—the kinetic temperature and microturbulent velocity—within the limits imposed by observations does not result in any substantial variations of the line profiles. The comparison between the model and observed spectra indicates that L1544 displays a flattened structure, and is viewed at an oblique angle. A two-dimensional model is needed to reproduce this structure.     

2D stabilised analytic signal method in DC pole-pole potential data interpretation

Using analytic signal method, interpretation of pole-pole secondary electric potentials due to 2D conductive/resistive prisms is presented. The estimated parameters are the location, lateral extent or width and depth to top surface of the prism. Forward modelling is attempted by 2D-Finite Difference method. The proposed stabilised analytic signal algorithm (RES2AS) uses Tikhonov’s regularization scheme and FFT routines. The algorithm is tested on three theoretical examples and field data from the campus of Roorkee University. The stability of RES2AS is also tested on synthetic error prone secondary pole-pole potential data.