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).     

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.     

Estimation of hydraulic permeability considering the micro morphology of rocks of the borehole YAXCOPOIL-1 (Impact crater Chicxulub,Mexico)

Internal surface, formation factor, Nuclear Magnetic Resonance (NMR)-T2 relaxation times and pore radius distributions were measured on representative core samples for the estimation of hydraulic permeability. Permeability is estimated using various versions of the classic Kozeny–Carman-equation (K–C) and a further development of K–C, the fractal PaRiS-model, taking into account the internal surface. In addition to grain and pore size distribution, directly connected to permeability, internal surface reflects the internal structure (“micro morphology”). Lithologies could be grouped with respect to differences in internal surface. Most melt rich impact breccia lithologies exhibit large internal surfaces, while Tertiary post-impact sediments and Cretaceous lithologies in displaced megablocks display smaller internal surfaces. Investigations with scanning electron microscopy confirm the correlation between internal surface and micro morphology. In addition to different versions of K–C, estimations by means of NMR, pore radius distributions and some gas permeability measurements serve for cross-checking and calibration. In general, the different estimations from the independent methods and the measurements are in satisfactory accordance. For Tertiary limestones and Suevites bulk with very high porosities (up to 35%) permeabilites between 10⁻¹⁴ and 10⁻¹⁶ m² are found, whereas in lower Suevite, Cretaceous anhydrites and dolomites, bulk permeabilites are between 10⁻¹⁵ and 10⁻²³ m².     

Automated Kerogen Classification in Microscope Images of Dispersed Kerogen Preparation

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naïve Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p<0.05) and indistinguishable from the other four classifiers. The initial set of 32 features was subsequently reduced to 6 features without compromising the classification accuracy. A further evaluation of the system alerted us to the possible sensitivity of the classification to the ground truth that might vary from one human expert to another. The analysis also revealed that the logistic classifier made most of the correct classifications with a high certainty.     

On the magnetism of stars and planets

A self-consistent statistical approach to the problem of planetary and stellar magnetism is suggested. The mechanism of magnetic field generation in the astronomical objects, where the existence of fields is associated with the axial rotation of objects, is discussed. In the general case the light pressure, the centrifugal, gravitational and other forces produce partial -separation of the charges. As a result of the system rotation, the magnetic fields of the currents of these charges are not compensated. The influence of various factors on the magnetic field of some object is analysed.