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Magnetic anomalies may be simply analyzed using a dike model. In this model, the depth to the source is approximately 1.25 S, where S is the straight-slope distance measured on the steepest flank. The dip of the body may be evaluated from e, the combined magnetic angle found from the ratio of the maximum amplitude to the minimum amplitude. In the presence of remanence, the body dip 8 may be found from e only when the direction of resultant magnetization is either known or assumed, and magnetization direction may be found only if the dip is known. The minimum susceptibility contrast k of the source may be calculated from the total amplitude A and intensity T of the inducing field using the relationship k = OJ AIr. A large number of aeromagnetic anomalies have been interpreted using this method. A few such anomalies are presented here to illustrate the applicability of the method.

RAMA RAO, CH., VIJAYAKUMAR, V., VIRUPAKSHI, G. and RAO, M.B.S.V. A Note on the Interpretation of Magnetic Anomalies of Infinite Thin Sheets by Using Relation Figures, Geophysical Prospecting 33,746752.The concept of relation figures is utilized in the case of a thin infinite sheet for identification and determination of the causative source geometry, and its various parameters. The plot of A T versus AV is a conic, the plots between the respective symmetric components and the respective asymmetric components are straight lines, and the resulting diagram between the first horizontal versus the first vertical derivative of either A T or AV is a cardioid.

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This paper proposes a novel approach for Structural health condition monitoring based on Neuro-Fuzzy systems (NFS). This can be done effectively by multilevel data acquisition process in Wireless Sensor Network (WSN). This is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. From that process identification of system anomalies and faults can be done easily. In standard control systems these abnormalities can be observed as gradually deviating trends from the normal behavior. Tools that are available for monitoring these trends, in some cases may not be enough to reveal hidden faulty features. For interpreting these changes accurately, measured data values must be visualized as a combination of multiple sensor signals within a particular domain. In this paper an approach to effectively utilize integrated data from multiple sources, and defines a set of fault features is given. The approach confidence values are set based on these interpretations to differentiate normal and abnormal conditions exhibited by the operating system. This is to provide an opportunity for the process and maintenance engineers to effectively identify the equipments health based on the early identification of developing abnormalities.