The middle to late Archean Iron Ore Group rocks occurring along the western margin (the Western Iron Ore basin) of the Singhbhum Granite massif in the Singhbhum craton were deformed during Iron Ore orogeny and are disposed in a horseshoe-shaped synclinal structure in the eastern part of the Indian shield. The Western Iron Ore basin hosts almost all the major high-grade iron ore deposits of eastern India. Contrary to the established view, present analysis emphasizes that the horseshoe fold in reality is a synclinorium consisting of a syncline–anticline fold pair which were later cross-folded along an east–west axis.
Structural analysis in the eastern anticline of the ‘horseshoe synclinorium’ suggests that the BIF hosting the high-grade iron ore bodies are disposed in three linear NNE–SSW trending belts, each showing an open synclinal geometry. Later cross folding produced development of widespread dome and basin pattern at the sub-horizontal hinge zones of these synclinal fold belts. The major iron ore deposits in the eastern anticline at the present level of erosion are preferentially localized within shallow elongated basinal structures only. The axis of the adjoining western syncline was similarly uplifted as partial culminations where cross-folded against E–W anticlinal axes. But here, the BIF-iron ore bodies are preferentially localized within elongated domal structures in contrast to the basinal sites in the adjacent eastern anticline. Such an inference based on structural analysis could probably be utilized as a potential tool for all future explorations, reserve estimation and recovery of the iron ore deposits in the terrain. 相似文献
The neural network system has been developing very fast recently. It has been widely used in many industries such as automation, nuclear power plant, chemical industry, etc. Neural network systems have a great advantage in dealing with problems in which many factors influence the process and result, and the understanding of this process is poor, and there are experimental data or field data. In rock engineering, many problems are of this nature. In this paper, a brief introduction to neural network systems is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in rock engineering, made by us, are presented. Case 1 is ore boundary delineation. In this case, the rock are divided into three classes, i.e.: (1) waste rock; (2) semi-ore; and (3) ore for mining purposes. The neural network system built can judge whether it is ore, semi-ore or waste rock along the borehole according its corresponding geophysical logging data, such as gamma-ray, gamma-gamma, neutron and resistivity. Case 2 is aggregate quality prediction. In this case, the quality parameters: (1) impact value; (2) abrasion value I; and (3) abrasion value II are predicted by using a neural network system based on density, point load, content of quarts and content of brittle minerals. Case 3 is rock indentation depth prediction. In this case, the rock indentation depth under indentation load is predicted by the established neural network system based on the indentation load on rock, indenter type and rock mechanical properties, such as uniaxial compressive strength, Young's modulus. Poisson's ratio, critical energy release rate and density. In all these cases, the neural network systems have been applied successfully. The testing results are satisfactory and better than the existing techniques. 相似文献
