Solar flares associated with singularities in the differential characteristics of magnetic fields

We analyze the flare events of October 28 and May 27, 2003 to examine the possible connection of solar flares with singularities in the differential characteristics of solar magnetic fields. The relation between flares and the behavior of F indicators (which describe structural singularities of the magnetic fields) introduced earlier is analyzed. It is shown that the generation of flares can be associated with self-intersections (or reconnection) of the F = 0 curves, where magnetic reconnection can occur. Consequently, the current sheets generated in such regions can be sources of energy release.     

Rotational and magnetic corrections to the g-mode frequencies in a one-dimensional MHD model

The central magnetic field and rotation of the solar radiative zone are responsible for corrections to the g-mode frequencies. Magnetogravitational spectra are calculated analytically in a simple one-dimensional MHD model that goes beyond the WKB approximation and avoid any cusp resonances that trap the wave within the radiative zone in the presence of a weak magnetic background. The calculations are compared with spacecraft observations of the 1% frequency shifts for candidate g-modes found in the SOHO GOLF experiment. The magnetic correction is the main contribution for a strong magnetic field satisfying the approximation used. It is shown that a constant magnetic field of 700 kG in the radiative zone provides the required frequency shift for the n = ?10 g-mode. The rotational correction, which is due to the Coriolis force in the one-dimensional model used, is much less than a percent (α_Ω ≤ 0.003).     

Uncertainty as the overlap of alternate conditional distributions

An important task in modern geostatistics is the assessment and quantification of resource and reserve uncertainty. This uncertainty is valuable support information for many management decisions. Uncertainty at specific locations and uncertainty in the global resource is of interest. There are many different methods to build models of uncertainty, including Kriging, Cokriging, and Inverse Distance. Each method leads to different results. A method is proposed to combine local uncertainties predicted by different models to obtain a combined measure of uncertainty that combines good features of each alternative. The new estimator is the overlap of alternate conditional distributions.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.